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Editors: Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, Sivan Sabato
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PAC-Bayesian Bounds on Rate-Efficient Classifiers
; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1-9
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Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10-32
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An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn
Emmanuel Abbe, Elisabetta Cornacchia, Jan Hazla, Christopher Marquis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:33-52
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Active Sampling for Min-Max Fairness
Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:53-65
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abubakar Abid, Mert Yuksekgonul, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:66-88
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Batched Dueling Bandits
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:89-110
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Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.
Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:111-135
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Deep equilibrium networks are sensitive to initialization statistics
Atish Agarwala, Samuel S Schoenholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:136-160
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Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:161-179
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On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:180-196
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Individual Preference Stability for Clustering
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:197-246
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Understanding the unstable convergence of gradient descent
Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:247-257
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Minimum Cost Intervention Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:258-289
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Ahmed Alaa, Boris Van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:290-306
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A Natural Actor-Critic Framework for Zero-Sum Markov Games
Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:307-366
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Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:367-393
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Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
Lucas Nunes Alegre, Ana Bazzan, Bruno C. Da Silva; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:394-413
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Structured Stochastic Gradient MCMC
Antonios Alexos, Alex J Boyd, Stephan Mandt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:414-434
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XAI for Transformers: Better Explanations through Conservative Propagation
Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:435-451
RUMs from Head-to-Head Contests
Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:452-467
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Uri Alon, Frank Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:468-485
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Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Verónica Álvarez, Santiago Mazuelas, Jose A Lozano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:486-499
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
Sebastian E Ament, Carla P Gomes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:500-516
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Public Data-Assisted Mirror Descent for Private Model Training
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:517-535
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On Last-Iterate Convergence Beyond Zero-Sum Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:536-581
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Online Algorithms with Multiple Predictions
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:582-598
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Learning to Hash Robustly, Guaranteed
Alexandr Andoni, Daniel Beaglehole; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:599-618
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Set Based Stochastic Subsampling
Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:619-638
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Towards Understanding Sharpness-Aware Minimization
Maksym Andriushchenko, Nicolas Flammarion; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:639-668
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Fair and Fast k-Center Clustering for Data Summarization
Haris Angelidakis, Adam Kurpisz, Leon Sering, Rico Zenklusen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:669-702
Interactive Correlation Clustering with Existential Cluster Constraints
Rico Angell, Nicholas Monath, Nishant Yadav, Andrew Mccallum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:703-716
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Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit Pal Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:717-730
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AdaGrad Avoids Saddle Points
Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras, Xiao Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:731-771
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UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Levy, Panayotis Mertikopoulos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:772-795
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Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antoran, David Janz, James U Allingham, Erik Daxberger, Riccardo Rb Barbano, Eric Nalisnick, Jose Miguel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:796-821
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EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:822-843
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Online Balanced Experimental Design
David Arbour, Drew Dimmery, Tung Mai, Anup Rao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:844-864
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VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty
Randy Ardywibowo, Zepeng Huo, Zhangyang Wang, Bobak J Mortazavi, Shuai Huang, Xiaoning Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:865-877
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Thresholded Lasso Bandit
Kaito Ariu, Kenshi Abe, Alexandre Proutiere; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:878-928
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Gradient Based Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic, Soummya Kar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:929-947
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Understanding Gradient Descent on the Edge of Stability in Deep Learning
Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:948-1024
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Private optimization in the interpolation regime: faster rates and hardness results
Hilal Asi, Karan Chadha, Gary Cheng, John Duchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1025-1045
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Optimal Algorithms for Mean Estimation under Local Differential Privacy
Hilal Asi, Vitaly Feldman, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1046-1056
Asymptotically-Optimal Gaussian Bandits with Side Observations
Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1057-1077
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Congested Bandits: Optimal Routing via Short-term Resets
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi, Kostas Kollias; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1078-1100
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Do More Negative Samples Necessarily Hurt In Contrastive Learning?
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1101-1116
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H-Consistency Bounds for Surrogate Loss Minimizers
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1117-1174
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Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
Kyriakos Axiotis, Maxim Sviridenko; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1175-1197
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Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün, Ankit Anand, Laurent Orseau, Xavier Glorot, Stephen M Mcaleer, Vlad Firoiu, Lei M Zhang, Doina Precup, Shibl Mourad; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1198-1210
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Near-optimal rate of consistency for linear models with missing values
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1211-1243
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How Tempering Fixes Data Augmentation in Bayesian Neural Networks
Gregor Bachmann, Lorenzo Noci, Thomas Hofmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1244-1260
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ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1261-1276
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From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
Heesun Bae, Seungjae Shin, Byeonghu Na, Joonho Jang, Kyungwoo Song, Il-Chul Moon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1277-1297
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data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1298-1312
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End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
Taha Bahadori, Eric Tchetgen Tchetgen, David Heckerman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1313-1326
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A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai, Lixin Cui, Hancock Edwin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1327-1336
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Near-Optimal Learning of Extensive-Form Games with Imperfect Information
Yu Bai, Chi Jin, Song Mei, Tiancheng Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1337-1382
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Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai, Shufeng Kong, Carla P Gomes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1383-1398
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A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
He Bai, Renjie Zheng, Junkun Chen, Mingbo Ma, Xintong Li, Liang Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1399-1411
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Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
Arindam Banerjee, Tiancong Chen, Xinyan Li, Yingxue Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1412-1449
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Certified Neural Network Watermarks with Randomized Smoothing
Arpit Bansal, Ping-Yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1450-1465
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Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1466-1482
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Learning Stable Classifiers by Transferring Unstable Features
Yujia Bao, Shiyu Chang, Dr.Regina Barzilay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1483-1507
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Fast Composite Optimization and Statistical Recovery in Federated Learning
Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1508-1536
Generative Modeling for Multi-task Visual Learning
Zhipeng Bao, Martial Hebert, Yu-Xiong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1537-1554
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Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1555-1584
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On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao, Yoshihiro Nagano, Kento Nozawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1585-1606
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Representation Topology Divergence: A Method for Comparing Neural Network Representations.
Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1607-1626
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Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation
Adarsh Barik, Jean Honorio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1627-1646
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Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time
Burak Bartan, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1647-1663
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
Lucas Baudin, Rida Laraki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1664-1690
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Information Discrepancy in Strategic Learning
Yahav Bechavod, Chara Podimata, Steven Wu, Juba Ziani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1691-1715
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M Sadler, Pratap Tokekar, Alec Koppel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1716-1731
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1732-1748
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Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel, Aditya Grover, Ricky T. Q. Chen, Yaron Lipman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1749-1763
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Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1764-1786
Neural Inverse Kinematic
Raphael Bensadoun, Shir Gur, Nitsan Blau, Lior Wolf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1787-1797
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Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Gregory Benton, Wesley Maddox, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1798-1816
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Gradient Descent on Neurons and its Link to Approximate Second-order Optimization
Frederik Benzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1817-1853
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Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, Francesco Trovò; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1854-1873
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Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
Peter Bevan, Amir Atapour-Abarghouei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1874-1892
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Approximate Bayesian Computation with Domain Expert in the Loop
Ayush Bharti, Louis Filstroff, Samuel Kaski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1893-1905
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Minimax M-estimation under Adversarial Contamination
Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1906-1924
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Nearly Optimal Catoni’s M-estimator for Infinite Variance
Sujay Bhatt, Guanhua Fang, Ping Li, Gennady Samorodnitsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1925-1944
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Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1945-1962
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Non-Vacuous Generalisation Bounds for Shallow Neural Networks
Felix Biggs, Benjamin Guedj; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1963-1981
Structure-preserving GANs
Jeremiah Birrell, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1982-2020
Scalable Spike-and-Slab
Niloy Biswas, Lester Mackey, Xiao-Li Meng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2021-2040
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Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2041-2074
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A query-optimal algorithm for finding counterfactuals
Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2075-2090
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Popular decision tree algorithms are provably noise tolerant
Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2091-2106
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Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Tom Blau, Edwin V. Bonilla, Iadine Chades, Amir Dezfouli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2107-2128
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Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
Huang Bojun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2129-2159
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model
Heejong Bong, Alessandro Rinaldo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2160-2177
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How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
Akhilan Boopathy, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2178-2205
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Improving Language Models by Retrieving from Trillions of Tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, Laurent Sifre; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2206-2240
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Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter, Max Welling, Daniel E Worrall; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2241-2256
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An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
Guillaume Braun, Hemant Tyagi, Christophe Biernacki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2257-2291
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Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner, Florian Hess, Jonas M Mikhaeil, Leonard F Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2292-2320
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Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Luc Brogat-Motte, Rémi Flamary, Celine Brouard, Juho Rousu, Florence D’Alché-Buc; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2321-2335
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Efficient Learning of CNNs using Patch Based Features
Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Schwartz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2336-2356
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Causal structure-based root cause analysis of outliers
Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, Dominik Janzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2357-2369
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulić; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2370-2392
Interactive Inverse Reinforcement Learning for Cooperative Games
Thomas Kleine Büning, Anne-Marie George, Christos Dimitrakakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2393-2413
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Convolutional and Residual Networks Provably Contain Lottery Tickets
Rebekka Burkholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2414-2433
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Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
Haoyuan Cai, Tengyu Ma, Simon Du; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2434-2456
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Convergence of Invariant Graph Networks
Chen Cai, Yusu Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2457-2484
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Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2485-2522
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Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2523-2541
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Adaptive Gaussian Process Change Point Detection
Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2542-2571
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Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2572-2596
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A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling
Yiting Cao, Chao Lan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2597-2608
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Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone, Armin Lederer, Sandra Hirche; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2609-2624
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Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning
Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2625-2637
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A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2638-2657
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RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2658-2685
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Estimating and Penalizing Induced Preference Shifts in Recommender Systems
Micah D Carroll, Anca Dragan, Stuart Russell, Dylan Hadfield-Menell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2686-2708
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YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone
Edresson Casanova, Julian Weber, Christopher D Shulby, Arnaldo Candido Junior, Eren Gölge, Moacir A Ponti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2709-2720
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The Infinite Contextual Graph Markov Model
Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2721-2737
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Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Timothy J Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2738-2766
Online Learning with Knapsacks: the Best of Both Worlds
Matteo Castiglioni, Andrea Celli, Christian Kroer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2767-2783
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Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Edoardo Cetin, Philip J Ball, Stephen Roberts, Oya Celiktutan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2784-2810
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Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
Karan Chadha, Gary Cheng, John Duchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2811-2827
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Robust Imitation Learning against Variations in Environment Dynamics
Jongseong Chae, Seungyul Han, Whiyoung Jung, Myungsik Cho, Sungho Choi, Youngchul Sung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2828-2852
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Fairness with Adaptive Weights
Junyi Chai, Xiaoqian Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2853-2866
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2867-2889
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Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao, Ngai-Man Cheung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2890-2916
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Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
Jen-Hao Rick Chang, Ashish Shrivastava, Hema Koppula, Xiaoshuai Zhang, Oncel Tuzel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2917-2937
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Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan Chang, Kaiwen Wang, Nathan Kallus, Wen Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2938-2971
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Sample Efficient Learning of Predictors that Complement Humans
Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2972-3005
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Nyström Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3006-3024
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3025-3039
Learning Domain Adaptive Object Detection with Probabilistic Teacher
Meilin Chen, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, Shiliang Pu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3040-3055
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The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz, Ananda Theertha Suresh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3056-3089
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Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Mayee Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3090-3122
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Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3123-3148
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On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs
Yuanzhou Chen, Jiafan He, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3149-3183
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Streaming Algorithms for Support-Aware Histograms
Justin Chen, Piotr Indyk, Tal Wagner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3184-3203
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
Liyu Chen, Rahul Jain, Haipeng Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3204-3245
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Learning Infinite-horizon Average-reward Markov Decision Process with Constraints
Liyu Chen, Rahul Jain, Haipeng Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3246-3270
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Active Multi-Task Representation Learning
Yifang Chen, Kevin Jamieson, Simon Du; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3271-3298
On Collective Robustness of Bagging Against Data Poisoning
Ruoxin Chen, Zenan Li, Jie Li, Junchi Yan, Chentao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3299-3319
Online Active Regression
Cheng Chen, Yi Li, Yiming Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3320-3335
Selling Data To a Machine Learner: Pricing via Costly Signaling
Junjie Chen, Minming Li, Haifeng Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3336-3359
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z Chen, Jian Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3360-3370
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Weisfeiler-Lehman Meets Gromov-Wasserstein
Samantha Chen, Sunhyuk Lim, Facundo Memoli, Zhengchao Wan, Yusu Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3371-3416
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On Non-local Convergence Analysis of Deep Linear Networks
Kun Chen, Dachao Lin, Zhihua Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3417-3443
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Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Li, Jianyu Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3444-3468
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Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O’Bray, Karsten Borgwardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3469-3489
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The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3490-3506
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Learning Mixtures of Linear Dynamical Systems
Yanxi Chen, H. Vincent Poor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3507-3557
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On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
Xiaohong Chen, Zhengling Qi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3558-3582
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Faster Fundamental Graph Algorithms via Learned Predictions
Justin Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3583-3602
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Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters
Xin Chen, Yujie Tang, Na Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3603-3620
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Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3621-3633
Auxiliary Learning with Joint Task and Data Scheduling
Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3634-3647
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Optimization-Induced Graph Implicit Nonlinear Diffusion
Qi Chen, Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3648-3661
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Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3662-3678
Adaptive Model Design for Markov Decision Process
Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3679-3700
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State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks
Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, Yonghong Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3701-3715
Efficient Online ML API Selection for Multi-Label Classification Tasks
Lingjiao Chen, Matei Zaharia, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3716-3746
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Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3747-3759
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3760-3772
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3773-3793
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Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3794-3834
Task-aware Privacy Preservation for Multi-dimensional Data
Jiangnan Cheng, Ao Tang, Sandeep Chinchali; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3835-3851
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Adversarially Trained Actor Critic for Offline Reinforcement Learning
Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3852-3878
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Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Nadiia Chepurko, Kenneth Clarkson, Lior Horesh, Honghao Lin, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3879-3900
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
Victor Chernozhukov, Whitney Newey, Vı́ctor M Quintas-Martı́nez, Vasilis Syrgkanis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3901-3914
Self-supervised learning with random-projection quantizer for speech recognition
Chung-Cheng Chiu, James Qin, Yu Zhang, Jiahui Yu, Yonghui Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3915-3924
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Discrete Probabilistic Inverse Optimal Transport
Wei-Ting Chiu, Pei Wang, Patrick Shafto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3925-3946
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Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, Chinmay Hegde; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3947-3961
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From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamas Sarlos, Adrian Weller, Thomas Weingarten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3962-3983
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Shuffle Private Linear Contextual Bandits
Sayak Ray Chowdhury, Xingyu Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3984-4009
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DNA: Domain Generalization with Diversified Neural Averaging
Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, Hong Mei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4010-4034
TPC: Transformation-Specific Smoothing for Point Cloud Models
Wenda Chu, Linyi Li, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4035-4056
Unified Scaling Laws for Routed Language Models
Aidan Clark, Diego De Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, George Bm Van Den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc’Aurelio Ranzato, Jack Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4057-4086
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Context-Aware Drift Detection
Oliver Cobb, Arnaud Van Looveren; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4087-4111
On the Robustness of CountSketch to Adaptive Inputs
Edith Cohen, Xin Lyu, Jelani Nelson, Tamas Sarlos, Moshe Shechner, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4112-4140
Diffusion bridges vector quantized variational autoencoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4141-4156
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Online and Consistent Correlation Clustering
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4157-4179
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Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances
Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4180-4201
One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams
Benjamin Coleman, Benito Geordie, Li Chou, R. A. Leo Elworth, Todd Treangen, Anshumali Shrivastava; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4202-4218
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4219-4237
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MAML and ANIL Provably Learn Representations
Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4238-4310
Entropic Causal Inference: Graph Identifiability
Spencer Compton, Kristjan Greenewald, Dmitriy A Katz, Murat Kocaoglu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4311-4343
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Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4344-4369
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Counterfactual Transportability: A Formal Approach
Juan D Correa, Sanghack Lee, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4370-4390
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Label-Free Explainability for Unsupervised Models
Jonathan Crabbé, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4391-4420
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Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, Taylan Cemgil; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4421-4435
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Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers
Francesco Croce, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4436-4454
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Self-conditioning Pre-Trained Language Models
Xavier Suau Cuadros, Luca Zappella, Nicholas Apostoloff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4455-4473
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Only tails matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4474-4491
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Principal Component Flows
Edmond Cunningham, Adam D Cobb, Susmit Jha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4492-4519
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Deep symbolic regression for recurrence prediction
Stéphane D’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, Francois Charton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4520-4536
Continuous Control with Action Quantization from Demonstrations
Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4537-4557
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Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Y Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4558-4586
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DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4587-4604
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Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization
Hanjun Dai, Mengjiao Yang, Yuan Xue, Dale Schuurmans, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4605-4617
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Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning
Angelo Damiani, Giorgio Manganini, Alberto Maria Metelli, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4618-4629
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Understanding Robust Generalization in Learning Regular Languages
Soham Dan, Osbert Bastani, Dan Roth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4630-4643
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Unsupervised Image Representation Learning with Deep Latent Particles
Tal Daniel, Aviv Tamar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4644-4665
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Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4666-4689
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Monarch: Expressive Structured Matrices for Efficient and Accurate Training
Tri Dao, Beidi Chen, Nimit S Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4690-4721
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Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4722-4753
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Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4754-4776
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Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew Mccallum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4777-4793
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Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4794-4815
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Distinguishing rule and exemplar-based generalization in learning systems
Ishita Dasgupta, Erin Grant, Tom Griffiths; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4816-4830
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Robust Multi-Objective Bayesian Optimization Under Input Noise
Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4831-4866
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Attentional Meta-learners for Few-shot Polythetic Classification
Ben J Day, Ramon Viñas Torné, Nikola Simidjievski, Pietro Lió; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4867-4889
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Adversarial Vulnerability of Randomized Ensembles
Hassan Dbouk, Naresh Shanbhag; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4890-4917
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Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Giuseppe Bruno De Luca, Eva Silverstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4918-4936
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
Giorgia Dellaferrera, Gabriel Kreiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4937-4955
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DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
Fei Deng, Ingook Jang, Sungjin Ahn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4956-4975
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NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng, Jiaxin Shi, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4976-4992
Deep Causal Metric Learning
Xiang Deng, Zhongfei Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:4993-5006
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On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5007-5038
Analysis of Stochastic Processes through Replay Buffers
Shirli Di-Castro, Shie Mannor, Dotan Di Castro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5039-5060
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Streaming Algorithms for High-Dimensional Robust Statistics
Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5061-5117
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Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5118-5141
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Variational Feature Pyramid Networks
Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5142-5152
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Understanding Doubly Stochastic Clustering
Tianjiao Ding, Derek Lim, Rene Vidal, Benjamin D Haeffele; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5153-5165
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Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Mihailo Jovanovic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5166-5220
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Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi, Samuele S Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5221-5285
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Fair Generalized Linear Models with a Convex Penalty
Hyungrok Do, Preston Putzel, Axel S Martin, Padhraic Smyth, Judy Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5286-5308
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Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan M. Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5309-5323
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On the Adversarial Robustness of Causal Algorithmic Recourse
Ricardo Dominguez-Olmedo, Amir H Karimi, Bernhard Schölkopf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5324-5342
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Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
Runpei Dong, Zhanhong Tan, Mengdi Wu, Linfeng Zhang, Kaisheng Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5343-5359
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PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5360-5377
Privacy for Free: How does Dataset Condensation Help Privacy?
Tian Dong, Bo Zhao, Lingjuan Lyu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5378-5396
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Fast rates for noisy interpolation require rethinking the effect of inductive bias
Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic, Fanny Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5397-5428
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Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Ron Dorfman, Kfir Yehuda Levy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5429-5446
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TACTiS: Transformer-Attentional Copulas for Time Series
Alexandre Drouin, Étienne Marcotte, Nicolas Chapados; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5447-5493
Branching Reinforcement Learning
Yihan Du, Wei Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5494-5530
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Bayesian Imitation Learning for End-to-End Mobile Manipulation
Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5531-5546
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GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P Bosma, Zongwei Zhou, Tao Wang, Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc Le, Yonghui Wu, Zhifeng Chen, Claire Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5547-5569
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Learning Iterative Reasoning through Energy Minimization
Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5570-5582
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SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5583-5608
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A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5609-5626
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Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B Paulus, Yangjun Ruan, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5627-5642
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Parametric Visual Program Induction with Function Modularization
Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5643-5658
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Bayesian Deep Embedding Topic Meta-Learner
Zhibin Duan, Yishi Xu, Jianqiao Sun, Bo Chen, Wenchao Chen, Chaojie Wang, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5659-5670
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Deletion Robust Submodular Maximization over Matroids
Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5671-5693
From data to functa: Your data point is a function and you can treat it like one
Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, Dan Rosenbaum; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5694-5725
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Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models
Valentin Durante, George Katsirelos, Thomas Schiex; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5726-5741
Robust Counterfactual Explanations for Tree-Based Ensembles
Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5742-5756
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On the Difficulty of Defending Self-Supervised Learning against Model Extraction
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5757-5776
LIMO: Latent Inceptionism for Targeted Molecule Generation
Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael Gilson, Rose Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5777-5792
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Inductive Biases and Variable Creation in Self-Attention Mechanisms
Benjamin L Edelman, Surbhi Goel, Sham Kakade, Cyril Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5793-5831
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Provable Reinforcement Learning with a Short-Term Memory
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5832-5850
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Sparsity in Partially Controllable Linear Systems
Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5851-5860
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5861-5877
pathGCN: Learning General Graph Spatial Operators from Paths
Moshe Eliasof, Eldad Haber, Eran Treister; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5878-5891
Discrete Tree Flows via Tree-Structured Permutations
Mai Elkady, Jim Lim, David I. Inouye; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5892-5923
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For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, Stuart Russell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5924-5943
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Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Alina Ene, Huy Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5944-5967
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Towards Scaling Difference Target Propagation by Learning Backprop Targets
Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5968-5987
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Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:5988-6008
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Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C Mozer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6009-6033
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Variational Sparse Coding with Learned Thresholding
Kion Fallah, Christopher J Rozell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6034-6058
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Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, Peter J Ramadge; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6059-6073
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DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
Jiameng Fan, Wenchao Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6074-6102
Generalized Data Distribution Iteration
Jiajun Fan, Changnan Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6103-6184
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Variational Wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6185-6215
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Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6216-6234
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Bayesian Continuous-Time Tucker Decomposition
Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6235-6245
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Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6246-6283
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6284-6323
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Investigating Generalization by Controlling Normalized Margin
Alexander R Farhang, Jeremy D Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6324-6336
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
Gabriele Farina, Chung-Wei Lee, Haipeng Luo, Christian Kroer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6337-6357
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Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
Oisin Faust, Hamza Fawzi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6358-6372
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Matching Structure for Dual Learning
Hao Fei, Shengqiong Wu, Yafeng Ren, Meishan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6373-6391
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Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning
Yingjie Fei, Ruitu Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6392-6417
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Private frequency estimation via projective geometry
Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6418-6433
An Intriguing Property of Geophysics Inversion
Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6434-6446
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Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6447-6464
A Resilient Distributed Boosting Algorithm
Yuval Filmus, Idan Mehalel, Shay Moran; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6465-6473
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Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, Andre Barreto, Simon Osindero; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6474-6498
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Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6499-6513
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Conformal Prediction Sets with Limited False Positives
Adam Fisch, Tal Schuster, Tommi Jaakkola, Dr.Regina Barzilay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6514-6532
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Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet, Claire Bizon Monroc, Karim Beguir, Thomas Pierrot; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6533-6547
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Fast Relative Entropy Coding with A* coding
Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6548-6577
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Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Adam Foster, Arpi Vezer, Craig A. Glastonbury, Paidi Creed, Samer Abujudeh, Aaron Sim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6578-6621
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Label Ranking through Nonparametric Regression
Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6622-6659
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A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi, Emmanuel De Bézenac, Ibrahim Ayed, Mickael Chen, Sylvain Lamprier, Patrick Gallinari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6660-6704
Extracting Latent State Representations with Linear Dynamics from Rich Observations
Abraham Frandsen, Rong Ge, Holden Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6705-6725
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SPDY: Accurate Pruning with Speedup Guarantees
Elias Frantar, Dan Alistarh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6726-6743
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Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6744-6778
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Bregman Neural Networks
Jordan Frecon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6779-6792
(Non-)Convergence Results for Predictive Coding Networks
Simon Frieder, Thomas Lukasiewicz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6793-6810
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Scaling Structured Inference with Randomization
Yao Fu, John Cunningham, Mirella Lapata; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6811-6828
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Greedy when Sure and Conservative when Uncertain about the Opponents
Haobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu, Jiechao Xiong, Ying Wen, Kai Li, Junliang Xing, Qiang Fu, Wei Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6829-6848
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DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6849-6862
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Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
Wei Fu, Chao Yu, Zelai Xu, Jiaqi Yang, Yi Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6863-6877
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$p$-Laplacian Based Graph Neural Networks
Guoji Fu, Peilin Zhao, Yatao Bian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6878-6917
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6918-6943
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Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6944-6959
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The Complexity of k-Means Clustering when Little is Known
Robert Ganian, Thekla Hamm, Viktoriia Korchemna, Karolina Okrasa, Kirill Simonov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6960-6987
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IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data
Tian Gao, Debarun Bhattacharjya, Elliot Nelson, Miao Liu, Yue Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:6988-7001
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Loss Function Learning for Domain Generalization by Implicit Gradient
Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7002-7016
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
Hongchang Gao, Junyi Li, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7017-7035
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Deep Reference Priors: What is the best way to pretrain a model?
Yansong Gao, Rahul Ramesh, Pratik Chaudhari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7036-7051
On the Equivalence Between Temporal and Static Equivariant Graph Representations
Jianfei Gao, Bruno Ribeiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7052-7076
Generalizing Gaussian Smoothing for Random Search
Katelyn Gao, Ozan Sener; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7077-7101
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Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
Yue Gao, Ilia Shumailov, Kassem Fawaz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7102-7121
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Lazy Estimation of Variable Importance for Large Neural Networks
Yue Gao, Abby Stevens, Garvesh Raskutti, Rebecca Willett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7122-7143
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Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7144-7163
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Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems
Lucy L Gao, Jane Ye, Haian Yin, Shangzhi Zeng, Jin Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7164-7182
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Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Xiang Gao, Yuqi Zhang, Yingjie Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207
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Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7208-7222
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PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, John Lygeros; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7223-7240
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The power of first-order smooth optimization for black-box non-smooth problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takac, Pavel Dvurechensky, Bin Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7241-7265
A Functional Information Perspective on Model Interpretation
Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7266-7278
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UniRank: Unimodal Bandit Algorithms for Online Ranking
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7279-7309
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Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Tomas Geffner, Justin Domke; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7310-7323
Inducing Causal Structure for Interpretable Neural Networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah Goodman, Christopher Potts; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7324-7338
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Achieving Minimax Rates in Pool-Based Batch Active Learning
Claudio Gentile, Zhilei Wang, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7339-7367
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Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7368-7381
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Online Learning for Min Sum Set Cover and Pandora’s Box
Evangelia Gergatsouli, Christos Tzamos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7382-7403
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Equivariance versus Augmentation for Spherical Images
Jan Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7404-7421
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A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7422-7434
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Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7435-7469
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Faster Privacy Accounting via Evolving Discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7470-7483
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Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7484-7512
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Offline RL Policies Should Be Trained to be Adaptive
Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7513-7530
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Breaking the $\sqrtT$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh, Abishek Sankararaman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7531-7549
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SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
Giorgio Giannone, Ole Winther; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7550-7569
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A Joint Exponential Mechanism For Differentially Private Top-$k$
Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7570-7582
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Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois, Zhaohui Jiang, Xuening Feng, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu, Jianye Hao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7583-7615
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It’s Raw! Audio Generation with State-Space Models
Karan Goel, Albert Gu, Chris Donahue, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7616-7633
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Yu Gong, Greg Mori, Fred Tung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7634-7649
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How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
Chengyue Gong, Lemeng Wu, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7650-7664
Partial Label Learning via Label Influence Function
Xiuwen Gong, Dong Yuan, Wei Bao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7665-7678
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Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin, Max Ryabinin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7679-7739
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Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C Humphreys, Ksenia Konyushova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7740-7765
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The State of Sparse Training in Deep Reinforcement Learning
Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7766-7792
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Causal Inference Through the Structural Causal Marginal Problem
Luigi Gresele, Julius Von Kügelgen, Jonas Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7793-7824
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Mirror Learning: A Unifying Framework of Policy Optimisation
Jakub Grudzien, Christian A Schroeder De Witt, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7825-7844
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Adapting k-means Algorithms for Outliers
Christoph Grunau, Václav Rozhoň; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7845-7886
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
Yichen Gu, David T Blaauw, Joshua Welch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7887-7901
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Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7902-7918
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NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Shanyan Guan, Huayu Deng, Yunbo Wang, Xiaokang Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7919-7929
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Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
Jiechao Guan, Zhiwu Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7930-7948
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
Lin Guan, Sarath Sreedharan, Subbarao Kambhampati; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7949-7967
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Large-Scale Graph Neural Architecture Search
Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7968-7981
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Identifiability Conditions for Domain Adaptation
Ishaan Gulrajani, Tatsunori Hashimoto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7982-7997
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A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi, Saurabh Kumar, Junfeng Wen, Dale Schuurmans; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7998-8015
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Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8016-8038
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No-Regret Learning in Partially-Informed Auctions
Wenshuo Guo, Michael Jordan, Ellen Vitercik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8039-8055
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Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8056-8071
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Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Chong Guo, Michael Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James Dicarlo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8072-8081
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Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding
Lan-Zhe Guo, Yu-Feng Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8082-8094
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Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
Alan J.X. Guo, Cong Liang, Qing-Hu Hou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8095-8108
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Online Continual Learning through Mutual Information Maximization
Yiduo Guo, Bing Liu, Dongyan Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8109-8126
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Fast Provably Robust Decision Trees and Boosting
Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8127-8144
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Understanding and Improving Knowledge Graph Embedding for Entity Alignment
Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8145-8156
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NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
Mustafa B Gurbuz, Constantine Dovrolis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8157-8174
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Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen, Avihu Dekel, Daphna Weinshall; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8175-8195
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You Only Cut Once: Boosting Data Augmentation with a Single Cut
Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8196-8212
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Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8213-8229
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G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8230-8248
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Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making
Yuxuan Han, Zhicong Liang, Zhipeng Liang, Yang Wang, Yuan Yao, Jiheng Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8249-8279
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Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8280-8303
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Adversarial Attacks on Gaussian Process Bandits
Eric Han, Jonathan Scarlett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8304-8329
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Random Gegenbauer Features for Scalable Kernel Methods
Insu Han, Amir Zandieh, Haim Avron; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8330-8358
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Stochastic Reweighted Gradient Descent
Ayoub El Hanchi, David Stephens, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8359-8374
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Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification
Jun-Yi Hang, Min-Ling Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8375-8386
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Temporal Difference Learning for Model Predictive Control
Nicklas A Hansen, Hao Su, Xiaolong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8387-8406
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Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair, Patrick Yin, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8407-8426
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TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8427-8445
Contextual Information-Directed Sampling
Botao Hao, Tor Lattimore, Chao Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8446-8464
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GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8465-8483
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
Kais Hariz, Hachem Kadri, Stephane Ayache, Maher Moakher, Thierry Artieres; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8484-8501
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Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Keegan Harris, Dung Daniel T Ngo, Logan Stapleton, Hoda Heidari, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8502-8522
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C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Yuka Hashimoto, Zhao Wang, Tomoko Matsui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8523-8534
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General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, Joao Carreira, Jesse Engel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8535-8558
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On Distribution Shift in Learning-based Bug Detectors
Jingxuan He, Luca Beurer-Kellner, Martin Vechev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8559-8580
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GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Yixuan He, Quan Gan, David Wipf, Gesine D Reinert, Junchi Yan, Mihai Cucuringu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8581-8612
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Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
Bobby He, Mete Ozay; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8613-8634
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Sparse Double Descent: Where Network Pruning Aggravates Overfitting
Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8635-8659
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A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds
Jiahao He, Jiheng Zhang, Rachel Q. Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8660-8677
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HyperPrompt: Prompt-based Task-Conditioning of Transformers
Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, Yaguang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8678-8690
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Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8691-8707
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NOMU: Neural Optimization-based Model Uncertainty
Jakob M Heiss, Jakob Weissteiner, Hanna S Wutte, Sven Seuken, Josef Teichmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8708-8758
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Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8759-8773
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Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael Mahoney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8774-8795
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
Valentin Hofmann, Janet Pierrehumbert, Hinrich Schütze; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8796-8810
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Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Samuel I Holt, Zhaozhi Qian, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8811-8832
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Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8833-8851
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
Robert Hönig, Yiren Zhao, Robert Mullins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8852-8866
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Vı́ctor Garcia Satorras, Clément Vignac, Max Welling; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8867-8887
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Conditional GANs with Auxiliary Discriminative Classifier
Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8888-8902
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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He, Qingming Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8903-8925
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Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling
Jiri Hron, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8926-8945
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Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8946-8970
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Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Pihe Hu, Yu Chen, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8971-9019
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Neuron Dependency Graphs: A Causal Abstraction of Neural Networks
Yaojie Hu, Jin Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9020-9040
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9041-9071
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On the Role of Discount Factor in Offline Reinforcement Learning
Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9072-9098
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Transformer Quality in Linear Time
Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc Le; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9099-9117
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Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9118-9147
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Forward Operator Estimation in Generative Models with Kernel Transfer Operators
Zhichun Huang, Rudrasis Chakraborty, Vikas Singh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9148-9172
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Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Jiatai Huang, Yan Dai, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9173-9200
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Frustratingly Easy Transferability Estimation
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9201-9225
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Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9226-9259
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Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9260-9279
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3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9280-9294
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SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Hu Xianghong, Jeffry Wicaksana, Eric Xing, Kwang-Ting Cheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9295-9309
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Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan, Jinming Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9310-9345
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Efficient Representation Learning via Adaptive Context Pooling
Chen Huang, Walter Talbott, Navdeep Jaitly, Joshua M Susskind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9346-9355
On the Learning of Non-Autoregressive Transformers
Fei Huang, Tianhua Tao, Hao Zhou, Lei Li, Minlie Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9356-9376
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Going Deeper into Permutation-Sensitive Graph Neural Networks
Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9377-9409
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9410-9428
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Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Geert-Jan Huizing, Laura Cantini, Gabriel Peyré; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9429-9443
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Robust Kernel Density Estimation with Median-of-Means principle
Pierre Humbert, Batiste Le Bars, Ludovic Minvielle; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9444-9465
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A data-driven approach for learning to control computers
Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy Lillicrap; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9466-9482
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Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault, Arthur Leclaire, Nicolas Papadakis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9483-9505
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Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9506-9524
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Datamodels: Understanding Predictions with Data and Data with Predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9525-9587
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Parsimonious Learning-Augmented Caching
Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9588-9601
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Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9602-9621
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
David Ireland, Giovanni Montana; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9622-9638
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The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9639-9659
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A Modern Self-Referential Weight Matrix That Learns to Modify Itself
Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9660-9677
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Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness
Shinji Ito; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9678-9694
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Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, Noam Brown; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9695-9728
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A deep convolutional neural network that is invariant to time rescaling
Brandon G Jacques, Zoran Tiganj, Aakash Sarkar, Marc Howard, Per Sederberg; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9729-9738
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Input Dependent Sparse Gaussian Processes
Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9739-9759
Regret Minimization with Performative Feedback
Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9760-9785
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Biological Sequence Design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9786-9801
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Combining Diverse Feature Priors
Saachi Jain, Dimitris Tsipras, Aleksander Madry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9802-9832
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Training Your Sparse Neural Network Better with Any Mask
Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9833-9844
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Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9845-9880
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Surrogate Likelihoods for Variational Annealed Importance Sampling
Martin Jankowiak, Du Phan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9881-9901
Planning with Diffusion for Flexible Behavior Synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9902-9915
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HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Daniel Jarrett, Bogdan C Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9916-9937
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Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
Adrian Javaloy, Maryam Meghdadi, Isabel Valera; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9938-9964
Towards understanding how momentum improves generalization in deep learning
Samy Jelassi, Yuanzhi Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:9965-10040
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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10041-10052
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10053-10067
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Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10068-10103
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Improving Policy Optimization with Generalist-Specialist Learning
Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, Hao Su; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10104-10119
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Translatotron 2: High-quality direct speech-to-speech translation with voice preservation
Ye Jia, Michelle Tadmor Ramanovich, Tal Remez, Roi Pomerantz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10120-10134
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Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards
Huiwen Jia, Cong Shi, Siqian Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10135-10160
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The Role of Deconfounding in Meta-learning
Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10161-10176
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Subspace Learning for Effective Meta-Learning
Weisen Jiang, James Kwok, Yu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10177-10194
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Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10195-10216
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Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10217-10227
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Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10228-10250
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Chi Jin, Qinghua Liu, Tiancheng Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10251-10279
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Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10280-10297
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Accelerated Federated Learning with Decoupled Adaptive Optimization
Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu, Ji Liu, Dejing Dou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10298-10322
Supervised Off-Policy Ranking
Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10323-10339
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Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin, Zeru Zhang, Yang Zhou, Lingfei Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10340-10361
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10362-10383
Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits
Marc Jourdan, Rémy Degenne; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10384-10430
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
Haotian Ju, Dongyue Li, Hongyang R Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10431-10461
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Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Justin Jude, Matthew Perich, Lee Miller, Matthias Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10462-10475
On Measuring Causal Contributions via do-interventions
Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10476-10501
Efficient Approximate Inference for Stationary Kernel on Frequency Domain
Yohan Jung, Kyungwoo Song, Jinkyoo Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10502-10538
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Sketching Algorithms and Lower Bounds for Ridge Regression
Praneeth Kacham, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10539-10556
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob D Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10557-10574
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
Jan Kaiser, Oliver Stein, Annika Eichler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10575-10585
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Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
Konstantinos Kalais, Sotirios Chatzis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10586-10597
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Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10598-10632
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Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
Gautam Kamath, Xingtu Liu, Huanyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10633-10660
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Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Hidetaka Kamigaito, Katsuhiko Hayashi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10661-10675
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Matching Learned Causal Effects of Neural Networks with Domain Priors
Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramanian, Amit Sharma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10676-10696
Deduplicating Training Data Mitigates Privacy Risks in Language Models
Nikhil Kandpal, Eric Wallace, Colin Raffel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10697-10707
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Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Katie Kang, Paula Gradu, Jason J Choi, Michael Janner, Claire Tomlin, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10708-10733
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Forget-free Continual Learning with Winning Subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, Chang D. Yoo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10734-10750
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Differentially Private Approximate Quantiles
Haim Kaplan, Shachar Schnapp, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10751-10761
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Simultaneous Graph Signal Clustering and Graph Learning
Abdullah Karaaslanli, Selin Aviyente; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10762-10772
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Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10773-10801
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Meta-Learning Hypothesis Spaces for Sequential Decision-making
Parnian Kassraie, Jonas Rothfuss, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10802-10824
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FOCUS: Familiar Objects in Common and Uncommon Settings
Priyatham Kattakinda, Soheil Feizi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10825-10847
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Training OOD Detectors in their Natural Habitats
Julian Katz-Samuels, Julia B Nakhleh, Robert Nowak, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10848-10865
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Robustness Implies Generalization via Data-Dependent Generalization Bounds
Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10866-10894
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Generating Distributional Adversarial Examples to Evade Statistical Detectors
Yigitcan Kaya, Muhammad Bilal Zafar, Sergul Aydore, Nathalie Rauschmayr, Krishnaram Kenthapadi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10895-10911
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Secure Quantized Training for Deep Learning
Marcel Keller, Ke Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10912-10938
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A Convergent and Dimension-Independent Min-Max Optimization Algorithm
Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10939-10973
Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10974-10996
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Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10997-11057
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Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim, Geeho Kim, Bohyung Han; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11058-11073
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Learning fair representation with a parametric integral probability metric
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11074-11101
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Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, Hyun Oh Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11102-11118
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Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Heeseung Kim, Sungwon Kim, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11119-11133
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Variational On-the-Fly Personalization
Jangho Kim, Jun-Tae Lee, Simyung Chang, Nojun Kwak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11134-11147
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Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Minyoung Kim, Da Li, Shell X Hu, Timothy Hospedales; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11148-11161
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ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
Sangwon Kim, Jaeyeal Nam, Byoung Chul Ko; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11162-11172
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Sanity Simulations for Saliency Methods
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11173-11200
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Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11201-11228
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Rotting Infinitely Many-Armed Bandits
Jung-Hun Kim, Milan Vojnovic, Se-Young Yun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11229-11254
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Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
Jungbin Kim, Insoon Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11255-11282
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Generalizing to New Physical Systems via Context-Informed Dynamics Model
Matthieu Kirchmeyer, Yuan Yin, Jeremie Dona, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11283-11301
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SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David A Clifton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11302-11340
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Curriculum Reinforcement Learning via Constrained Optimal Transport
Pascal Klink, Haoyi Yang, Carlo D’Eramo, Jan Peters, Joni Pajarinen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11341-11358
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Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
David M. Knigge, David W Romero, Erik J Bekkers; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11359-11386
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Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11387-11412
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Transfer Learning In Differential Privacy’s Hybrid-Model
Refael Kohen, Or Sheffet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11413-11429
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems
Lukas Köhs, Bastian Alt, Heinz Koeppl; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11430-11454
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Partial disentanglement for domain adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11455-11472
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback
Fang Kong, Yichi Zhou, Shuai Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11473-11482
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Adaptive Data Analysis with Correlated Observations
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11483-11498
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Controlling Conditional Language Models without Catastrophic Forgetting
Tomasz Korbak, Hady Elsahar, German Kruszewski, Marc Dymetman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11499-11528
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Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity
Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11529-11558
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Certified Adversarial Robustness Under the Bounded Support Set
Yiwen Kou, Qinyuan Zheng, Yisen Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11559-11597
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Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
Sonja Kraiczy, Edith Elkind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11598-11612
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Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
Daniel Kramer, Philine L Bommer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11613-11633
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Probabilistic ODE Solutions in Millions of Dimensions
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11634-11649
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Active Nearest Neighbor Regression Through Delaunay Refinement
Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11650-11664
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Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11665-11682
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Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Volodymyr Kuleshov, Shachi Deshpande; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11683-11693
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ActiveHedge: Hedge meets Active Learning
Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11694-11709
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Anand Kulkarni, Varun Jampani, Venkatesh Babu Radhakrishnan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11710-11728
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Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
Vladislav Kurenkov, Sergey Kolesnikov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11729-11752
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Equivariant Priors for compressed sensing with unknown orientation
Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11753-11771
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Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11772-11789
Large Batch Experience Replay
Thibault Lahire, Matthieu Geist, Emmanuel Rachelson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11790-11813
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, Mosharaf Chowdhury; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11814-11827
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Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data
Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S Cheung, Chen-Nee Chuah; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11828-11843
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Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
Alex Lambert, Dimitri Bouche, Zoltan Szabo, Florence D’Alché-Buc; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11844-11867
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Tell me why! Explanations support learning relational and causal structure
Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison Tam, James Mcclelland, Chen Yan, Adam Santoro, Neil C Rabinowitz, Jane Wang, Felix Hill; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11868-11890
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Generative Cooperative Networks for Natural Language Generation
Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11891-11905
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DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11906-11917
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Cooperative Online Learning in Stochastic and Adversarial MDPs
Tal Lancewicki, Aviv Rosenberg, Yishay Mansour; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11918-11968
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PINs: Progressive Implicit Networks for Multi-Scale Neural Representations
Zoe Landgraf, Alexander Sorkine Hornung, Ricardo S Cabral; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11969-11984
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Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica N Agrawal, Yoon Kim, David Sontag; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11985-12003
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Goal Misgeneralization in Deep Reinforcement Learning
Lauro Langosco Di Langosco, Jack Koch, Lee D Sharkey, Jacob Pfau, David Krueger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12004-12019
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Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12020-12048
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Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes
Tim Tsz-Kit Lau, Han Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12049-12077
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Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Lauriere, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Perolat, Romuald Elie, Olivier Pietquin, Matthieu Geist; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12078-12095
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Implicit Bias of Linear Equivariant Networks
Hannah Lawrence, Kristian Georgiev, Andrew Dienes, Bobak T. Kiani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12096-12125
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Differentially Private Maximal Information Coefficients
John Lazarsfeld, Aaron Johnson, Emmanuel Adeniran; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12126-12163
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Entropic Gromov-Wasserstein between Gaussian Distributions
Khang Le, Dung Q Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12164-12203
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Neurocoder: General-Purpose Computation Using Stored Neural Programs
Hung Le, Svetha Venkatesh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12204-12221
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Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime
James-Michael Leahy, Bekzhan Kerimkulov, David Siska, Lukasz Szpruch; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12222-12252
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A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources
Hugo Lebeau, Romain Couillet, Florent Chatelain; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12253-12281
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Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
Jongmin Lee, Joo Young Choi, Ernest K Ryu, Albert No; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12282-12351
Dataset Condensation with Contrastive Signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12352-12364
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Confidence Score for Source-Free Unsupervised Domain Adaptation
Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12365-12377
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A Statistical Manifold Framework for Point Cloud Data
Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12378-12402
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Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions
Eunsang Lee, Joon-Woo Lee, Junghyun Lee, Young-Sik Kim, Yongjune Kim, Jong-Seon No, Woosuk Choi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12403-12422
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Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
Yoonhyung Lee, Sungdong Lee, Joong-Ho Won; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12423-12454
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Maslow’s Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation
Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew Saxe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12455-12477
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Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12478-12497
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Least Squares Estimation using Sketched Data with Heteroskedastic Errors
Sokbae Lee, Serena Ng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12498-12520
Why the Rich Get Richer? On the Balancedness of Random Partition Models
Changwoo J Lee, Huiyan Sang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12521-12541
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Model Selection in Batch Policy Optimization
Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12542-12569
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Supervised Learning with General Risk Functionals
Liu Leqi, Audrey Huang, Zachary Lipton, Kamyar Azizzadenesheli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12570-12592
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Generalized Strategic Classification and the Case of Aligned Incentives
Sagi Levanon, Nir Rosenfeld; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12593-12618
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A Simple Unified Framework for High Dimensional Bandit Problems
Wenjie Li, Adarsh Barik, Jean Honorio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12619-12655
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Robust Training of Neural Networks Using Scale Invariant Architectures
Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank Reddi, Sanjiv Kumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12656-12684
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Spatial-Channel Token Distillation for Vision MLPs
Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12685-12695
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An Analytical Update Rule for General Policy Optimization
Hepeng Li, Nicholas Clavette, Haibo He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12696-12716
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On Convergence of Gradient Descent Ascent: A Tight Local Analysis
Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12717-12740
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On the Finite-Time Performance of the Knowledge Gradient Algorithm
Yanwen Li, Siyang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12741-12764
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Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12765-12781
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G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters
Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12782-12796
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Decomposing Temporal High-Order Interactions via Latent ODEs
Shibo Li, Robert Kirby, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12797-12812
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Neural Inverse Transform Sampler
Henry Li, Yuval Kluger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12813-12825
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PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Changbin Li, Suraj Kothawade, Feng Chen, Rishabh Iyer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12826-12842
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Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning
Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12843-12856
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C-MinHash: Improving Minwise Hashing with Circulant Permutation
Xiaoyun Li, Ping Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12857-12887
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BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12888-12900
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Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(ε^-7/4)$ Complexity
Huan Li, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12901-12916
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Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li, Hongfu Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12917-12930
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High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails
Shaojie Li, Yong Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12931-12963
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MetAug: Contrastive Learning via Meta Feature Augmentation
Jiangmeng Li, Wenwen Qiang, Changwen Zheng, Bing Su, Hui Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12964-12978
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PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12979-12997
CerDEQ: Certifiable Deep Equilibrium Model
Mingjie Li, Yisen Wang, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:12998-13013
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Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13014-13051
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Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13052-13065
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Difference Advantage Estimation for Multi-Agent Policy Gradients
Yueheng Li, Guangming Xie, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13066-13085
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Private Adaptive Optimization with Side information
Tian Li, Manzil Zaheer, Sashank Reddi, Virginia Smith; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13086-13105
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Permutation Search of Tensor Network Structures via Local Sampling
Chao Li, Junhua Zeng, Zerui Tao, Qibin Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13106-13124
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Hessian-Free High-Resolution Nesterov Acceleration For Sampling
Ruilin Li, Hongyuan Zha, Molei Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13125-13162
Double Sampling Randomized Smoothing
Linyi Li, Jiawei Zhang, Tao Xie, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13163-13208
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13209-13224
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Learning Multiscale Transformer Models for Sequence Generation
Bei Li, Tong Zheng, Yi Jing, Chengbo Jiao, Tong Xiao, Jingbo Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13225-13241
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Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13242-13256
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Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang, Michael Mahoney, Liam Hodgkinson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13257-13270
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling
Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13271-13284
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Litian Liang, Yaosheng Xu, Stephen Mcaleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13285-13301
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TSPipe: Learn from Teacher Faster with Pipelines
Hwijoon Lim, Yechan Kim, Sukmin Yun, Jinwoo Shin, Dongsu Han; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13302-13312
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Order Constraints in Optimal Transport
Yu Chin Fabian Lim, Laura Wynter, Shiau Hong Lim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13313-13333
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Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xueyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, Luc Van Gool; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13334-13343
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Federated Learning with Positive and Unlabeled Data
Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13344-13355
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Decentralized Online Convex Optimization in Networked Systems
Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13356-13393
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Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Jing Lin, Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Youliang Yan, Xueyi Zou, Yulun Zhang, Luc Van Gool; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13394-13404
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Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13405-13430
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Learning Augmented Binary Search Trees
Honghao Lin, Tian Luo, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13431-13440
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13441-13467
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Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13468-13504
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Interactively Learning Preference Constraints in Linear Bandits
David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13505-13527
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Delayed Reinforcement Learning by Imitation
Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13528-13556
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CITRIS: Causal Identifiability from Temporal Intervened Sequences
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, Stratis Gavves; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13557-13603
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StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Adam Liska, Tomas Kocisky, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien De Masson D’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-Mcmahon, Sophia Austin, Phil Blunsom, Angeliki Lazaridou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13604-13622
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Distributionally Robust $Q$-Learning
Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13623-13643
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Constrained Variational Policy Optimization for Safe Reinforcement Learning
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li, Ding Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13644-13668
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Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13669-13703
Boosting Graph Structure Learning with Dummy Nodes
Xin Liu, Jiayang Cheng, Yangqiu Song, Xin Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13704-13716
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Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent
Weiming Liu, Huacong Jiang, Bin Li, Houqiang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13717-13745
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Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781
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Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13782-13792
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Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13793-13806
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Rethinking Attention-Model Explainability through Faithfulness Violation Test
Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong, Jing Li, Shiqi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13807-13824
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Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13825-13856
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Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning
Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13857-13869
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Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy
Zhihan Liu, Miao Lu, Zhaoran Wang, Michael Jordan, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13870-13911
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Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13912-13924
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Communication-efficient Distributed Learning for Large Batch Optimization
Rui Liu, Barzan Mozafari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13925-13946
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Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13947-13994
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
Xingyu Liu, Deepak Pathak, Kris Kitani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13995-14007
Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots
Yuanyuan Liu, Fanhua Shang, Weixin An, Hongying Liu, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14008-14035
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Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
Qinghua Liu, Yuanhao Wang, Chi Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14036-14053
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Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14054-14072
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Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander Schwing; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14073-14093
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Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14094-14138
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GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael Mahoney, Alvin Cheung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14139-14152
Robust Training under Label Noise by Over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu, Chong You; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14153-14172
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Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14173-14196
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On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
Robert Loftin, Frans A Oliehoek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14197-14209
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AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long, Zheng Wang, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael Mahoney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14210-14222
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Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14223-14247
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Feature Learning and Signal Propagation in Deep Neural Networks
Yizhang Lou, Chris E Mingard, Soufiane Hayou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14248-14282
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
Bruno Loureiro, Cedric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14283-14314
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A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
Songtao Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14315-14357
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Additive Gaussian Processes Revisited
Xiaoyu Lu, Alexis Boukouvalas, James Hensman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14358-14383
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ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Yupu Lu, Shijie Lin, Guanqi Chen, Jia Pan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14384-14397
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Model-Free Opponent Shaping
Christopher Lu, Timon Willi, Christian A Schroeder De Witt, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14398-14411
Multi-slots Online Matching with High Entropy
Xingyu Lu, Qintong Wu, Wenliang Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14412-14428
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Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14429-14460
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Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Ekdeep Lubana, Chi Ian Tang, Fahim Kawsar, Robert Dick, Akhil Mathur; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14461-14484
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
Daniel D Lundstrom, Tianjian Huang, Meisam Razaviyayn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14485-14508
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BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression
Zhao Tang Luo, Huiyan Sang, Bani Mallick; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14509-14526
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Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, Tieniu Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14527-14541
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Channel Importance Matters in Few-Shot Image Classification
Xu Luo, Jing Xu, Zenglin Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14542-14559
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Learning Dynamics and Generalization in Deep Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14560-14581
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On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
Qi Lyu, Xiao Fu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14582-14600
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Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14601-14638
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Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Yecheng Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14639-14663
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Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14664-14698
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Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
Jan Macdonald, Mathieu E. Besançon, Sebastian Pokutta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14699-14716
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A Tighter Analysis of Spectral Clustering, and Beyond
Peter Macgregor, He Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14717-14742
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Zero-Shot Reward Specification via Grounded Natural Language
Parsa Mahmoudieh, Deepak Pathak, Trevor Darrell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14743-14752
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Feature selection using e-values
Subhabrata Majumdar, Snigdhansu Chatterjee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14753-14773
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Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
Bodhisattwa Prasad Majumder, Oana Camburu, Thomas Lukasiewicz, Julian Mcauley; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14786-14801
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Nonparametric Involutive Markov Chain Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14802-14859
Architecture Agnostic Federated Learning for Neural Networks
Disha Makhija, Xing Han, Nhat Ho, Joydeep Ghosh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14860-14870
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Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
Cedric Malherbe, Kevin Scaman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14871-14886
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More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Osman Asif Malik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14887-14917
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Unaligned Supervision for Automatic Music Transcription in The Wild
Ben Maman, Amit H Bermano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14918-14934
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Decision-Focused Learning: Through the Lens of Learning to Rank
Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14935-14947
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Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14948-14978
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
Tudor Manole, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14979-15006
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On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao, Lin Yang, Kaiqing Zhang, Tamer Basar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15007-15049
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On the Effects of Artificial Data Modification
Antonia Marcu, Adam Prugel-Bennett; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15050-15069
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Personalized Federated Learning through Local Memorization
Othmane Marfoq, Giovanni Neglia, Richard Vidal, Laetitia Kameni; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15070-15092
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Nested Bandits
Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier, Houssam Zenati; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15093-15121
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Closed-Form Diffeomorphic Transformations for Time Series Alignment
Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15122-15158
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15159-15179
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Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15180-15195
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Continual Repeated Annealed Flow Transport Monte Carlo
Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15196-15219
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How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation
Augustine Mavor-Parker, Kimberly Young, Caswell Barry, Lewis Griffin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15220-15240
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How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
Mantas Mazeika, Bo Li, David Forsyth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15241-15254
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Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder, Xiang Meng, Haoyue Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15255-15277
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Optimizing Tensor Network Contraction Using Reinforcement Learning
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15278-15292
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Causal Transformer for Estimating Counterfactual Outcomes
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15293-15329
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Steerable 3D Spherical Neurons
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15330-15339
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Transformers are Meta-Reinforcement Learners
Luckeciano C Melo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15340-15359
ButterflyFlow: Building Invertible Layers with Butterfly Matrices
Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15360-15375
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In defense of dual-encoders for neural ranking
Aditya Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15376-15400
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Equivariant Quantum Graph Circuits
Peter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15401-15420
Stochastic Rising Bandits
Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15421-15457
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Minimizing Control for Credit Assignment with Strong Feedback
Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15458-15483
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A Dynamical System Perspective for Lipschitz Neural Networks
Laurent Meunier, Blaise J Delattre, Alexandre Araujo, Alexandre Allauzen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15484-15500
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Distribution Regression with Sliced Wasserstein Kernels
Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15501-15523
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Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Siqi Miao, Mia Liu, Pan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15524-15543
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Modeling Structure with Undirected Neural Networks
Tsvetomila Mihaylova, Vlad Niculae, Andre Martins; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15544-15560
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Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15561-15583
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Learning Stochastic Shortest Path with Linear Function Approximation
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15584-15629
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Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15630-15649
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POEM: Out-of-Distribution Detection with Posterior Sampling
Yifei Ming, Ying Fan, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15650-15665
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A Simple Reward-free Approach to Constrained Reinforcement Learning
Sobhan Miryoosefi, Chi Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15666-15698
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Wide Neural Networks Forget Less Catastrophically
Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, Mehrdad Farajtabar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15699-15717
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Proximal and Federated Random Reshuffling
Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15718-15749
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ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15750-15769
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Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
Aaron Mishkin, Arda Sahiner, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15770-15816
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Memory-Based Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15817-15831
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Invariant Ancestry Search
Phillip B Mogensen, Nikolaj Thams, Jonas Peters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15832-15857
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Differentially Private Community Detection for Stochastic Block Models
Mohamed S Mohamed, Dung Nguyen, Anil Vullikanti, Ravi Tandon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15858-15894
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A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity
Michinari Momma, Chaosheng Dong, Jia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15895-15907
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EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15908-15926
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Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15927-15958
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Power-Law Escape Rate of SGD
Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15959-15975
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Rethinking Fano’s Inequality in Ensemble Learning
Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Nobuo Nukaga; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:15976-16016
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SpeqNets: Sparsity-aware permutation-equivariant graph networks
Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16017-16042
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CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
Yao Mark Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16043-16061
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Generalized Beliefs for Cooperative AI
Darius Muglich, Luisa M Zintgraf, Christian A Schroeder De Witt, Shimon Whiteson, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16062-16082
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Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis
Alexander Munteanu, Simon Omlor, Zhao Song, David Woodruff; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16083-16122
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Constants Matter: The Performance Gains of Active Learning
Stephen O Mussmann, Sanjoy Dasgupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16123-16173
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On the Generalization Analysis of Adversarial Learning
Waleed Mustafa, Yunwen Lei, Marius Kloft; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16174-16196
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Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya K Muthukumar, Akshay Krishnamurthy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16197-16222
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The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti, Riccardo De Santi, Marcello Restelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16223-16239
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PAC-Net: A Model Pruning Approach to Inductive Transfer Learning
Sanghoon Myung, In Huh, Wonik Jang, Jae Myung Choe, Jisu Ryu, Daesin Kim, Kee-Eung Kim, Changwook Jeong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16240-16252
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AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16253-16269
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Implicit Bias of the Step Size in Linear Diagonal Neural Networks
Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16270-16295
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DNNR: Differential Nearest Neighbors Regression
Youssef Nader, Leon Sixt, Tim Landgraf; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16296-16317
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Overcoming Oscillations in Quantization-Aware Training
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16318-16330
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Strategic Representation
Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen, Nir Rosenfeld; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16331-16352
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Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16353-16367
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Measuring Representational Robustness of Neural Networks Through Shared Invariances
Vedant Nanda, Till Speicher, Camila Kolling, John P Dickerson, Krishna Gummadi, Adrian Weller; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16368-16382
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Tight and Robust Private Mean Estimation with Few Users
Shyam Narayanan, Vahab Mirrokni, Hossein Esfandiari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16383-16412
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Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava, John Z Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert Kevin Katzschmann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16413-16427
Multi-Task Learning as a Bargaining Game
Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16428-16446
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Variational Inference for Infinitely Deep Neural Networks
Achille Nazaret, David Blei; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16447-16461
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Stable Conformal Prediction Sets
Eugene Ndiaye; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16462-16479
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Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum, Pulkit Agrawal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16480-16495
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Sublinear-Time Clustering Oracle for Signed Graphs
Stefan Neumann, Pan Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16496-16528
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Improved Regret for Differentially Private Exploration in Linear MDP
Dung Daniel T Ngo, Giuseppe Vietri, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16529-16552
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A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh X Nguyen, Yonatan Bisk, Hal Daumé Iii; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16553-16568
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Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
Tung Nguyen, Aditya Grover; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16569-16594
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Improving Transformers with Probabilistic Attention Keys
Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard Baraniuk, Nhat Ho, Stanley Osher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16595-16621
On Transportation of Mini-batches: A Hierarchical Approach
Khai Nguyen, Dang Nguyen, Quoc Dinh Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16622-16655
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Improving Mini-batch Optimal Transport via Partial Transportation
Khai Nguyen, Dang Nguyen, The-Anh Vu-Le, Tung Pham, Nhat Ho; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16656-16690
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Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16691-16723
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Optimal Estimation of Policy Gradient via Double Fitted Iteration
Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang, Mengdi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16724-16783
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, Mark Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16784-16804
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16805-16827
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The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16828-16847
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Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann D Gaebler, Ravi Shroff, Sharad Goel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16848-16887
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Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16888-16905
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Generative Trees: Adversarial and Copycat
Richard Nock, Mathieu Guillame-Bert; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16906-16951
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Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules
Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee, Honglak Lee, Yousung Jung; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16952-16968
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16969-16989
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Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N Gomez, Debora Marks, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16990-17017
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Fast Finite Width Neural Tangent Kernel
Roman Novak, Jascha Sohl-Dickstein, Samuel S Schoenholz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17018-17044
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Multicoated Supermasks Enhance Hidden Networks
Yasuyuki Okoshi, Ángel López Garcı́a-Arias, Kazutoshi Hirose, Kota Ando, Kazushi Kawamura, Thiem Van Chu, Masato Motomura, Jaehoon Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17045-17055
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Generalized Leverage Scores: Geometric Interpretation and Applications
Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17056-17070
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Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
Lorenzo Orecchia, Konstantinos Ameranis, Charalampos Tsourakakis, Kunal Talwar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17071-17093
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Anticorrelated Noise Injection for Improved Generalization
Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17094-17116
Scalable Deep Gaussian Markov Random Fields for General Graphs
Joel Oskarsson, Per Sidén, Fredrik Lindsten; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17117-17137
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Zero-shot AutoML with Pretrained Models
Ekrem Öztürk, Fabio Ferreira, Hadi Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17138-17155
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History Compression via Language Models in Reinforcement Learning
Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-Zadeh, Sepp Hochreiter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17156-17185
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A Study on the Ramanujan Graph Property of Winning Lottery Tickets
Bithika Pal, Arindam Biswas, Sudeshna Kolay, Pabitra Mitra, Biswajit Basu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17186-17201
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On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17202-17220
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Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17221-17237
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A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
Yu Pan, Zeyong Su, Ao Liu, Wang Jingquan, Nannan Li, Zenglin Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17238-17257
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Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17258-17277
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Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi, Kris Cao, Tomas Kocisky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17278-17294
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Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
Theodore P Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17295-17322
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A Theoretical Comparison of Graph Neural Network Extensions
Pál András Papp, Roger Wattenhofer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17323-17345
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Validating Causal Inference Methods
Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17346-17358
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The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17359-17371
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Learning Symmetric Embeddings for Equivariant World Models
Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem Van De Meent, Robin Walters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17372-17389
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Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Namuk Park, Songkuk Kim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17390-17419
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Exact Optimal Accelerated Complexity for Fixed-Point Iterations
Jisun Park, Ernest K Ryu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17420-17457
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Kernel Methods for Radial Transformed Compositional Data with Many Zeros
Junyoung Park, Changwon Yoon, Cheolwoo Park, Jeongyoun Ahn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17458-17472
Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17473-17498
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Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps
Alexandre Pasquiou, Yair Lakretz, John T Hale, Bertrand Thirion, Christophe Pallier; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17499-17516
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A new similarity measure for covariate shift with applications to nonparametric regression
Reese Pathak, Cong Ma, Martin Wainwright; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17517-17530
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Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies, Johannes Brandstetter, José Arjona-Medina, Sepp Hochreiter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17531-17572
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POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica, Joseph Gonzalez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17573-17583
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Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
Max B Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris Maddison; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17584-17600
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Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks
Franco Pellegrini, Giulio Biroli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17601-17626
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Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Yifan Peng, Siddharth Dalmia, Ian Lane, Shinji Watanabe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17627-17643
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Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17644-17655
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Differentiable Top-k Classification Learning
Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17656-17668
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Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17669-17690
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A Differential Entropy Estimator for Training Neural Networks
Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17691-17715
Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17716-17758
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Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi, Riccardo Zecchina; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17759-17781
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Geometric Multimodal Contrastive Representation Learning
Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17782-17800
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Constrained Offline Policy Optimization
Nicholas Polosky, Bruno C. Da Silva, Madalina Fiterau, Jithin Jagannath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17801-17810
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H Pong, Ashvin V Nair, Laura M Smith, Catherine Huang, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17811-17829
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Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17830-17847
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Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Pooladzandi, David Davini, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17848-17869
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On the Practicality of Deterministic Epistemic Uncertainty
Janis Postels, Mattia Segù, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, Federico Tombari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17870-17909
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A Simple Guard for Learned Optimizers
Isabeau Prémont-Schwarz, Jaroslav Vı́tků, Jan Feyereisl; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17910-17925
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Hardness and Algorithms for Robust and Sparse Optimization
Eric Price, Sandeep Silwal, Samson Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17926-17944
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Nonlinear Feature Diffusion on Hypergraphs
Konstantin Prokopchik, Austin R Benson, Francesco Tudisco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17945-17958
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
Michael Puthawala, Matti Lassas, Ivan Dokmanic, Maarten De Hoop; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17959-17983
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The Teaching Dimension of Regularized Kernel Learners
Hong Qian, Xu-Hui Liu, Chen-Xi Su, Aimin Zhou, Yang Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:17984-18002
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David Cox, Mark Hasegawa-Johnson, Shiyu Chang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18003-18017
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Interventional Contrastive Learning with Meta Semantic Regularizer
Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18018-18030
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Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18031-18061
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Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Tiexin Qin, Shiqi Wang, Haoliang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18062-18082
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Graph Neural Architecture Search Under Distribution Shifts
Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18083-18095
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Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty
Jixiang Qing, Tom Dhaene, Ivo Couckuyt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18096-18121
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Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18122-18152
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Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18153-18167
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Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18168-18210
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Fast and Provable Nonconvex Tensor RPCA
Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18211-18249
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Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, Zhuo Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18250-18280
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Particle Transformer for Jet Tagging
Huilin Qu, Congqiao Li, Sitian Qian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18281-18292
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Winning the Lottery Ahead of Time: Efficient Early Network Pruning
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18293-18309
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Convergence of Uncertainty Sampling for Active Learning
Anant Raj, Francis Bach; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18310-18331
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DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18332-18346
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Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
Alexandre Rame, Corentin Dancette, Matthieu Cord; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18347-18377
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A Closer Look at Smoothness in Domain Adversarial Training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, Venkatesh Babu Radhakrishnan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18378-18399
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Linear Adversarial Concept Erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan D Cotterell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18400-18421
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Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Noam Razin, Asaf Maman, Nadav Cohen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18422-18462
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One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18463-18482
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Universality of Winning Tickets: A Renormalization Group Perspective
William T Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18483-18498
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The dynamics of representation learning in shallow, non-linear autoencoders
Maria Refinetti, Sebastian Goldt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18499-18519
Proximal Exploration for Model-guided Protein Sequence Design
Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, Jian Peng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18520-18536
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Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18537-18558
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Benchmarking and Analyzing Point Cloud Classification under Corruptions
Jiawei Ren, Liang Pan, Ziwei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18559-18575
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A Unified View on PAC-Bayes Bounds for Meta-Learning
Arezou Rezazadeh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18576-18595
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3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Peter Richtarik, Igor Sokolov, Elnur Gasanov, Ilyas Fatkhullin, Zhize Li, Eduard Gorbunov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18596-18648
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Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
Lorenz Richter, Julius Berner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18649-18666
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Probabilistically Robust Learning: Balancing Average and Worst-case Performance
Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18667-18686
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LyaNet: A Lyapunov Framework for Training Neural ODEs
Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18687-18703
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Short-Term Plasticity Neurons Learning to Learn and Forget
Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18704-18722
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Function-space Inference with Sparse Implicit Processes
Simon Rodrı́guez-Santana, Bryan Zaldivar, Daniel Hernandez-Lobato; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18723-18740
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18741-18753
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Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
Tom Ron, Tamir Hazan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18754-18769
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A Consistent and Efficient Evaluation Strategy for Attribution Methods
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, Enkelejda Kasneci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18770-18795
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Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries
Daniel J Rosenkrantz, Abhijin Adiga, Madhav Marathe, Zirou Qiu, S S Ravi, Richard Stearns, Anil Vullikanti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18796-18808
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Learning to Infer Structures of Network Games
Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18809-18827
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Direct Behavior Specification via Constrained Reinforcement Learning
Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Chris J Pal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18828-18843
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Constraint-based graph network simulator
Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18844-18870
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Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18871-18887
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Graph-Coupled Oscillator Networks
T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18888-18909
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Hindering Adversarial Attacks with Implicit Neural Representations
Andrei A Rusu, Dan Andrei Calian, Sven Gowal, Raia Hadsell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18910-18934
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Exploiting Independent Instruments: Identification and Distribution Generalization
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18935-18958
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FedNL: Making Newton-Type Methods Applicable to Federated Learning
Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:18959-19010
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
Aadirupa Saha, Pierre Gaillard; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19011-19026
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Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
Aadirupa Saha, Shubham Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19027-19049
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Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Arda Sahiner, Tolga Ergen, Batu Ozturkler, John Pauly, Morteza Mardani, Mert Pilanci; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19050-19088
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito, Thorsten Joachims; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19089-19122
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Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr, Steve Dai, Rangha Venkatesan, Brian Zimmer, William Dally, Brucek Khailany; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19123-19138
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A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1
Adil Salim, Lukang Sun, Peter Richtarik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19139-19152
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FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers
Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y Singh, Zhao Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19153-19177
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The Algebraic Path Problem for Graph Metrics
Enrique Fita Sanmartı́n, Sebastian Damrich, Fred Hamprecht; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19178-19204
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LSB: Local Self-Balancing MCMC in Discrete Spaces
Emanuele Sansone; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19205-19220
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PoF: Post-Training of Feature Extractor for Improving Generalization
Ikuro Sato, Yamada Ryota, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19221-19230
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Re-evaluating Word Mover’s Distance
Ryoma Sato, Makoto Yamada, Hisashi Kashima; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19231-19249
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Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19250-19286
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The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
Andrew Saxe, Shagun Sodhani, Sam Jay Lewallen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19287-19309
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Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
Kevin Scaman, Cedric Malherbe, Ludovic Dos Santos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19310-19327
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An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Meyer Scetbon, Laurent Meunier, Yaniv Romano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19328-19346
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Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
Meyer Scetbon, Gabriel Peyré, Marco Cuturi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19347-19365
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Streaming Inference for Infinite Feature Models
Rylan Schaeffer, Yilun Du, Gabrielle K Liu, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19366-19387
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Modeling Irregular Time Series with Continuous Recurrent Units
Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19388-19405
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Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
Steffen Schotthöfer, Tianbai Xiao, Martin Frank, Cory Hauck; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19406-19433
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern Eskofier, Dario Zanca; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19434-19449
Symmetric Machine Theory of Mind
Melanie Sclar, Graham Neubig, Yonatan Bisk; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19450-19466
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Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19467-19496
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Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19497-19521
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Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
Mariia Seleznova, Gitta Kutyniok; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19522-19560
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Reinforcement Learning with Action-Free Pre-Training from Videos
Younggyo Seo, Kimin Lee, Stephen L James, Pieter Abbeel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19561-19579
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Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19580-19597
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Selective Regression under Fairness Criteria
Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W Wornell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19598-19615
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Utility Theory for Sequential Decision Making
Mehran Shakerinava, Siamak Ravanbakhsh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19616-19625
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Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19626-19644
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A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
Archit Sharma, Rehaan Ahmad, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19645-19657
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Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold
Sugandha Sharma, Sarthak Chandra, Ila Fiete; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19658-19682
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Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod Varshney; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19683-19730
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DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19731-19746
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Instance Dependent Regret Analysis of Kernelized Bandits
Shubhanshu Shekhar, Tara Javidi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19747-19772
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Data Augmentation as Feature Manipulation
Ruoqi Shen, Sebastien Bubeck, Suriya Gunasekar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19773-19808
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Metric-Fair Active Learning
Jie Shen, Nan Cui, Jing Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19809-19826
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PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Zhengyang Shen, Tao Hong, Qi She, Jinwen Ma, Zhouchen Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19827-19846
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen, Robbie M Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. Haochen, Tengyu Ma, Percy Liang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19847-19878
Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense
Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, Xiangyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19879-19892
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Staged Training for Transformer Language Models
Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew Peters, Iz Beltagy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19893-19908
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Deep Network Approximation in Terms of Intrinsic Parameters
Zuowei Shen, Haizhao Yang, Shijun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19909-19934
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Gradient-Free Method for Heavily Constrained Nonconvex Optimization
Wanli Shi, Hongchang Gao, Bin Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19935-19955
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Global Optimization of K-Center Clustering
Mingfei Shi, Kaixun Hua, Jiayang Ren, Yankai Cao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19956-19966
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:19967-20025
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Adversarial Masking for Self-Supervised Learning
Yuge Shi, N Siddharth, Philip Torr, Adam R Kosiorek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20026-20040
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Visual Attention Emerges from Recurrent Sparse Reconstruction
Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20041-20056
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A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20057-20094
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Robust Group Synchronization via Quadratic Programming
Yunpeng Shi, Cole M Wyeth, Gilad Lerman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20095-20105
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Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald, Justin M Solomon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20106-20124
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Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20125-20140
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Bit Prioritization in Variational Autoencoders via Progressive Coding
Rui Shu, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20141-20155
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Fair Representation Learning through Implicit Path Alignment
Changjian Shui, Qi Chen, Jiaqi Li, Boyu Wang, Christian Gagné; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20156-20175
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Faster Algorithms for Learning Convex Functions
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L Geyer, Venkatesh Saligrama, Brian Kulis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20176-20194
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Coin Flipping Neural Networks
Yuval Sieradzki, Nitzan Hodos, Gal Yehuda, Assaf Schuster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20195-20214
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Reverse Engineering the Neural Tangent Kernel
James Benjamin Simon, Sajant Anand, Mike Deweese; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20215-20231
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Demystifying the Adversarial Robustness of Random Transformation Defenses
Chawin Sitawarin, Zachary J Golan-Strieb, David Wagner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20232-20252
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Smoothed Adversarial Linear Contextual Bandits with Knapsacks
Vidyashankar Sivakumar, Shiliang Zuo, Arindam Banerjee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20253-20277
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GenLabel: Mixup Relabeling using Generative Models
Jy-Yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20278-20313
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Communicating via Markov Decision Processes
Samuel Sokota, Christian A Schroeder De Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, Martin Strohmeier, Zico Kolter, Shimon Whiteson, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20314-20328
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul, Kevin S Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20329-20346
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Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
Kyunghwan Son, Junsu Kim, Sungsoo Ahn, Roben D Delos Reyes, Yung Yi, Jinwoo Shin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20347-20368
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
Jaeyun Song, Joonhyung Park, Eunho Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20369-20383
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A General Recipe for Likelihood-free Bayesian Optimization
Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20384-20404
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Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
Sho Sonoda, Isao Ishikawa, Masahiro Ikeda; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20405-20422
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Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, Haitham Ammar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20423-20443
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Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Pedro J Soto, Ilia Ilmer, Haibin Guan, Jun Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20444-20458
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Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20459-20478
3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20479-20502
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EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20503-20521
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Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, Kristian Kersting; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20522-20545
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Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20546-20579
Divergence-Regularized Multi-Agent Actor-Critic
Kefan Su, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20580-20603
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Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems
Miguel Suau, Jinke He, Matthijs T. J. Spaan, Frans Oliehoek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20604-20624
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Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images
Rakshith Subramanyam, Vivek Narayanaswamy, Mark Naufel, Andreas Spanias, Jayaraman J. Thiagarajan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20625-20639
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Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems
Jaewook J Suh, Gyumin Roh, Ernest K Ryu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20640-20667
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Do Differentiable Simulators Give Better Policy Gradients?
Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20668-20696
Intriguing Properties of Input-Dependent Randomized Smoothing
Peter Súkenı́k, Aleksei Kuvshinov, Stephan Günnemann; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20697-20743
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Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Ryan Sullivan, Jordan K Terry, Benjamin Black, John P Dickerson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20744-20776
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AGNAS: Attention-Guided Micro and Macro-Architecture Search
Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20777-20789
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Adaptive Random Walk Gradient Descent for Decentralized Optimization
Tao Sun, Dongsheng Li, Bao Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20790-20809
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MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, Rong Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20810-20826
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Out-of-Distribution Detection with Deep Nearest Neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20827-20840
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Black-Box Tuning for Language-Model-as-a-Service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, Xipeng Qiu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20841-20855
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Correlated Quantization for Distributed Mean Estimation and Optimization
Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20856-20876
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Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20877-20890
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Being Properly Improper
Tyler Sypherd, Richard Nock, Lalitha Sankar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20891-20932
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Distributionally-Aware Kernelized Bandit Problems for Risk Aversion
Sho Takemori; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20933-20959
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound
Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20960-20986
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
Yuhta Takida, Takashi Shibuya, Weihsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20987-21012
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A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21013-21036
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N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21037-21049
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Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning
Yunhao Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21050-21075
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Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21076-21089
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Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
Huayi Tang, Yong Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21090-21110
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21111-21132
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Cross-Space Active Learning on Graph Convolutional Networks
Yufei Tao, Hao Wu, Shiyuan Deng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21133-21145
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FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21146-21179
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity
Sebastian Shenghong Tay, Chuan Sheng Foo, Urano Daisuke, Richalynn Leong, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21180-21204
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor, Adam Golinski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21205-21231
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LCANets: Lateral Competition Improves Robustness Against Corruption and Attack
Michael Teti, Garrett Kenyon, Ben Migliori, Juston Moore; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21232-21252
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Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees
Darshan Thaker, Paris Giampouras, Rene Vidal; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21253-21271
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Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Remi Munos, Andre Barreto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21272-21307
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Algorithms for the Communication of Samples
Lucas Theis, Noureldin Y Ahmed; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21308-21328
Consistent Polyhedral Surrogates for Top-k Classification and Variants
Anish Thilagar, Rafael Frongillo, Jessica J Finocchiaro, Emma Goodwill; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21329-21359
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On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
Lai Tian, Kaiwen Zhou, Anthony Man-Cho So; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21360-21379
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From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Menard; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21380-21431
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
Conor Tillinghast, Zheng Wang, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21432-21448
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Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm
Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21449-21477
Extended Unconstrained Features Model for Exploring Deep Neural Collapse
Tom Tirer, Joan Bruna; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21478-21505
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Object Permanence Emerges in a Random Walk along Memory
Pavel Tokmakov, Allan Jabri, Jie Li, Adrien Gaidon; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21506-21519
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Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
Elad Tolochinksy, Ibrahim Jubran, Dan Feldman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21520-21547
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Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
Umberto M Tomasini, Antonio Sclocchi, Matthieu Wyart; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21548-21583
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Quantifying and Learning Linear Symmetry-Based Disentanglement
Loek Tonnaer, Luis Armando Perez Rey, Vlado Menkovski, Mike Holenderski, Jim Portegies; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21584-21608
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A Temporal-Difference Approach to Policy Gradient Estimation
Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21609-21632
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Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher J Tosh, Daniel Hsu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21633-21657
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Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21658-21676
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AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco, Mariano Phielipp, Glen Berseth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21677-21691
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Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
Florian Tramer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21692-21702
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Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
Trang H Tran, Katya Scheinberg, Lam M Nguyen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21703-21732
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A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation
Chau Tran, Guo Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21733-21750
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Tackling covariate shift with node-based Bayesian neural networks
Trung Q Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21751-21775
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Fenrir: Physics-Enhanced Regression for Initial Value Problems
Filip Tronarp, Nathanael Bosch, Philipp Hennig; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21776-21794
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Interpretable Off-Policy Learning via Hyperbox Search
Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21795-21827
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FriendlyCore: Practical Differentially Private Aggregation
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21828-21863
Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding
Kazuma K Tsuji, Ken’Ichiro Tanaka, Sebastian Pokutta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21864-21883
Prototype Based Classification from Hierarchy to Fairness
Mycal Tucker, Julie A. Shah; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21884-21900
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Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Nelson Vadori, Rahul Savani, Thomas Spooner, Sumitra Ganesh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21901-21926
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Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech
Aditya R Vaidya, Shailee Jain, Alexander Huth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21927-21944
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Path-Gradient Estimators for Continuous Normalizing Flows
Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21945-21959
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Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21960-21983
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EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben Itzhak, Michael Mitzenmacher; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21984-22014
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Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22015-22059
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Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
Nate Veldt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22060-22083
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The CLRS Algorithmic Reasoning Benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22084-22102
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Bregman Power k-Means for Clustering Exponential Family Data
Adithya Vellal, Saptarshi Chakraborty, Jason Q Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22103-22119
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Ramji Venkataramanan, Kevin Kögler, Marco Mondelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22120-22144
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Bayesian Optimization under Stochastic Delayed Feedback
Arun Verma, Zhongxiang Dai, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22145-22167
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VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
Tathagat Verma, Abir De, Yateesh Agrawal, Vishwa Vinay, Soumen Chakrabarti; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22168-22183
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Calibrated Learning to Defer with One-vs-All Classifiers
Rajeev Verma, Eric Nalisnick; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22184-22202
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Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R Srikant; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22203-22233
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On Implicit Bias in Overparameterized Bilevel Optimization
Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, Roger B Grosse; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22234-22259
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Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22260-22269
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Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam R Villaflor, Zhe Huang, Swapnil Pande, John M Dolan, Jeff Schneider; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22270-22283
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Bayesian Nonparametrics for Offline Skill Discovery
Valentin Villecroze, Harry Braviner, Panteha Naderian, Chris Maddison, Gabriel Loaiza-Ganem; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22284-22299
Hermite Polynomial Features for Private Data Generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mi Jung Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22300-22324
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What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
Tiffany J Vlaar, Jonathan Frankle; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22325-22341
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Multirate Training of Neural Networks
Tiffany J Vlaar, Benedict Leimkuhler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22342-22360
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Provably Adversarially Robust Nearest Prototype Classifiers
Václav Voráček, Matthias Hein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22361-22383
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First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22384-22429
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Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22430-22456
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Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
Stephan Wäldchen, Sebastian Pokutta, Felix Huber; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22457-22474
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Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer
Yue Wan, Chang-Yu Hsieh, Ben Liao, Shengyu Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22475-22490
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Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan, Branislav Kveton, Rui Song; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22491-22511
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Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan, Nanning Zheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22512-22535
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Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm H Van Seijen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22536-22561
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Fast Lossless Neural Compression with Integer-Only Discrete Flows
Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22562-22575
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Accelerating Shapley Explanation via Contributive Cooperator Selection
Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22576-22590
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Denoised MDPs: Learning World Models Better Than the World Itself
Tongzhou Wang, Simon Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22591-22612
Neural Implicit Dictionary Learning via Mixture-of-Expert Training
Peihao Wang, Zhiwen Fan, Tianlong Chen, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22613-22624
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Robust Models Are More Interpretable Because Attributions Look Normal
Zifan Wang, Matt Fredrikson, Anupam Datta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22625-22651
Disentangling Disease-related Representation from Obscure for Disease Prediction
Chu-Ran Wang, Fei Gao, Fandong Zhang, Fangwei Zhong, Yizhou Yu, Yizhou Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22652-22664
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Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation
Jiali Wang, Wen Huang, Rujun Jiang, Xudong Li, Alex L Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22665-22679
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VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22680-22690
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DynaMixer: A Vision MLP Architecture with Dynamic Mixing
Ziyu Wang, Wenhao Jiang, Yiming M Zhu, Li Yuan, Yibing Song, Wei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22691-22701
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Improving Screening Processes via Calibrated Subset Selection
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22702-22726
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The Geometry of Robust Value Functions
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou, Bryan Hooi, Jiashi Feng, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22727-22751
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What Dense Graph Do You Need for Self-Attention?
Yuxin Wang, Chu-Tak Lee, Qipeng Guo, Zhangyue Yin, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22752-22768
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
Wenxiao Wang, Alexander J Levine, Soheil Feizi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22769-22783
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
Haoxiang Wang, Bo Li, Han Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22784-22801
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Communication-Efficient Adaptive Federated Learning
Yujia Wang, Lu Lin, Jinghui Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22802-22838
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Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, Bin Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22839-22864
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Robustness Verification for Contrastive Learning
Zekai Wang, Weiwei Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22865-22883
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
Peng Wang, Huikang Liu, Anthony Man-Cho So, Laura Balzano; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22884-22918
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NP-Match: When Neural Processes meet Semi-Supervised Learning
Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Alexandros Neophytou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22919-22934
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Iterative Double Sketching for Faster Least-Squares Optimization
Rui Wang, Yanyan Ouyang, Wangli Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22935-22963
What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22964-22984
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Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22985-22998
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Risk-Averse No-Regret Learning in Online Convex Games
Zifan Wang, Yi Shen, Michael Zavlanos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22999-23017
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Provable Domain Generalization via Invariant-Feature Subspace Recovery
Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23018-23033
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ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Hui-Po Wang, Sebastian Stich, Yang He, Mario Fritz; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23034-23054
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Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
Qi Wang, Herke Van Hoof; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23055-23077
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Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Rui Wang, Robin Walters, Rose Yu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23078-23091
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Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points
Yi Wang, Zhiren Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23092-23113
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang, Xin Eric Wang, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23114-23130
Tractable Uncertainty for Structure Learning
Benjie Wang, Matthew R Wicker, Marta Kwiatkowska; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23131-23150
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Causal Dynamics Learning for Task-Independent State Abstraction
Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, Peter Stone; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23151-23180
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Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
Xuchuang Wang, Hong Xie, John C. S. Lui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23181-23212
Generative Coarse-Graining of Molecular Conformations
Wujie Wang, Minkai Xu, Chen Cai, Benjamin K Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gomez-Bombarelli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23213-23236
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Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23237-23253
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When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23254-23273
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DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks
Zhuang Wang, Zhaozhuo Xu, Xinyu Wu, Anshumali Shrivastava, T. S. Eugene Ng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23274-23291
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Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications
Bokun Wang, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23292-23317
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OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23318-23340
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How Powerful are Spectral Graph Neural Networks
Xiyuan Wang, Muhan Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23341-23362
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Thompson Sampling for Robust Transfer in Multi-Task Bandits
Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23363-23416
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Individual Reward Assisted Multi-Agent Reinforcement Learning
Li Wang, Yupeng Zhang, Yujing Hu, Weixun Wang, Chongjie Zhang, Yang Gao, Jianye Hao, Tangjie Lv, Changjie Fan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23417-23432
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Removing Batch Normalization Boosts Adversarial Training
Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23433-23445
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Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J Smola, Zhangyang Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23446-23458
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Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Zheng Wang, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23459-23469
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Thompson Sampling for (Combinatorial) Pure Exploration
Siwei Wang, Jun Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23470-23483
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Policy Gradient Method For Robust Reinforcement Learning
Yue Wang, Shaofeng Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23484-23526
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Certifying Out-of-Domain Generalization for Blackbox Functions
Maurice G Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23527-23548
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More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
Alexander Wei, Wei Hu, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23549-23588
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To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23589-23614
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Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, Bo An; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23615-23630
Mitigating Neural Network Overconfidence with Logit Normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, Yixuan Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23631-23644
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Kawahara Yoshinobu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23645-23667
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Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23668-23684
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BabelTower: Learning to Auto-parallelized Program Translation
Yuanbo Wen, Qi Guo, Qiang Fu, Xiaqing Li, Jianxing Xu, Yanlin Tang, Yongwei Zhao, Xing Hu, Zidong Du, Ling Li, Chao Wang, Xuehai Zhou, Yunji Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23685-23700
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Random Forest Density Estimation
Hongwei Wen, Hanyuan Hang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23701-23722
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Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23723-23750
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Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23751-23780
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Measure Estimation in the Barycentric Coding Model
Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M Murphy; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23781-23803
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COLA: Consistent Learning with Opponent-Learning Awareness
Timon Willi, Alistair Hp Letcher, Johannes Treutlein, Jakob Foerster; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23804-23831
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Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Harley E Wiltzer, David Meger, Marc G. Bellemare; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23832-23856
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Easy Variational Inference for Categorical Models via an Independent Binary Approximation
Michael T Wojnowicz, Shuchin Aeron, Eric L Miller, Michael Hughes; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23857-23896
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Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk, Karol Piczak, Bartosz Wójcik, Lukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzcinski, Przemysław Spurek; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23897-23911
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A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Lukas Wolf, Ard Kastrati, Martyna B Plomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23912-23932
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Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time
David Woodruff, Amir Zandieh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23933-23964
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23965-23998
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Metric-Fair Classifier Derandomization
Jimmy Wu, Yatong Chen, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23999-24016
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Structural Entropy Guided Graph Hierarchical Pooling
Junran Wu, Xueyuan Chen, Ke Xu, Shangzhe Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24017-24030
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Self-supervised Models are Good Teaching Assistants for Vision Transformers
Haiyan Wu, Yuting Gao, Yinqi Zhang, Shaohui Lin, Yuan Xie, Xing Sun, Ke Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24031-24042
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Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks
Nan Wu, Stanislaw Jastrzebski, Kyunghyun Cho, Krzysztof J Geras; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24043-24055
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Instrumental Variable Regression with Confounder Balancing
Anpeng Wu, Kun Kuang, Bo Li, Fei Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24056-24075
MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution
Kailu Wu, Chung-Kuei Lee, Kaisheng Ma; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24076-24092
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Delay-Adaptive Step-sizes for Asynchronous Learning
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, Mikael Johansson; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24093-24113
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Variational nearest neighbor Gaussian process
Luhuan Wu, Geoff Pleiss, John P Cunningham; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24114-24130
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Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach
Shuang Wu, Ling Shi, Jun Wang, Guangjian Tian; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24131-24149
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DAVINZ: Data Valuation using Deep Neural Networks at Initialization
Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24150-24176
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Junlin Wu, Yevgeniy Vorobeychik; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24177-24211
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Revisiting Consistency Regularization for Deep Partial Label Learning
Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24212-24225
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Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24226-24242
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Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du, Jiantao Jiao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24243-24265
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RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Yihan Wu, Hongyang Zhang, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24266-24279
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24280-24314
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Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Jinghui Xia, Zengfeng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24315-24331
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ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z. Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24332-24346
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Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
Lechao Xiao, Jeffrey Pennington; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24347-24369
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Identification of Linear Non-Gaussian Latent Hierarchical Structure
Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24370-24387
COAT: Measuring Object Compositionality in Emergent Representations
Sirui Xie, Ari S Morcos, Song-Chun Zhu, Ramakrishna Vedantam; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24388-24413
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Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24414-24429
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Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24430-24459
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Self-Supervised Representation Learning via Latent Graph Prediction
Yaochen Xie, Zhao Xu, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24460-24477
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Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24478-24495
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A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24496-24523
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Importance Weighted Kernel Bayes’ Rule
Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24524-24538
Learning to Separate Voices by Spatial Regions
Alan Xu, Romit Roy Choudhury; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24539-24549
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Detached Error Feedback for Distributed SGD with Random Sparsification
An Xu, Heng Huang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24550-24575
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Accurate Quantization of Measures via Interacting Particle-based Optimization
Lantian Xu, Anna Korba, Dejan Slepcev; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24576-24595
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Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24596-24614
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Inferring Cause and Effect in the Presence of Heteroscedastic Noise
Sascha Xu, Osman A Mian, Alexander Marx, Jilles Vreeken; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24615-24630
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Prompting Decision Transformer for Few-Shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, Chuang Gan; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24631-24645
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Analyzing and Mitigating Interference in Neural Architecture Search
Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Jian Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24646-24662
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On the Statistical Benefits of Curriculum Learning
Ziping Xu, Ambuj Tewari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24663-24682
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A Difference Standardization Method for Mutual Transfer Learning
Haoqing Xu, Meng Wang, Beilun Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24683-24697
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu, Karl D.D. Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, Yasutaka Furukawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24698-24724
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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24725-24742
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Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24743-24769
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Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Xiaojun Xu, Jacky Y Zhang, Evelyn Ma, Hyun Ho Son, Sanmi Koyejo, Bo Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24770-24802
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24803-24829
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Langevin Monte Carlo for Contextual Bandits
Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24830-24850
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Investigating Why Contrastive Learning Benefits Robustness against Label Noise
Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24851-24871
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Diversified Adversarial Attacks based on Conjugate Gradient Method
Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Toru Mitsutake, Issa Oe, Hiroki Ishikura, Katsuki Fujisawa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24872-24894
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Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24895-24910
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Optimally Controllable Perceptual Lossy Compression
Zeyu Yan, Fei Wen, Peilin Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24911-24928
Active fairness auditing
Tom Yan, Chicheng Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24929-24962
Self-Organized Polynomial-Time Coordination Graphs
Qianlan Yang, Weijun Dong, Zhizhou Ren, Jianhao Wang, Tonghan Wang, Chongjie Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24963-24979
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
Shentao Yang, Yihao Feng, Shujian Zhang, Mingyuan Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:24980-25006
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A Psychological Theory of Explainability
Scott Cheng-Hsin Yang, Nils Erik Tomas Folke, Patrick Shafto; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25007-25021
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Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Ling Yang, Shenda Hong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25022-25037
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Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Ling Yang, Shenda Hong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25038-25054
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Searching for BurgerFormer with Micro-Meso-Macro Space Design
Longxing Yang, Yu Hu, Shun Lu, Zihao Sun, Jilin Mei, Yinhe Han, Xiaowei Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25055-25069
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Efficient Variance Reduction for Meta-learning
Hansi Yang, James Kwok; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25070-25095
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Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Zhun Yang, Joohyung Lee, Chiyoun Park; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25096-25122
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Locally Sparse Neural Networks for Tabular Biomedical Data
Junchen Yang, Ofir Lindenbaum, Yuval Kluger; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25123-25153
Not All Poisons are Created Equal: Robust Training against Data Poisoning
Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25154-25165
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Does the Data Induce Capacity Control in Deep Learning?
Rubing Yang, Jialin Mao, Pratik Chaudhari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25166-25197
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Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity
Jianyi Yang, Shaolei Ren; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25198-25240
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Linear Bandit Algorithms with Sublinear Time Complexity
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25241-25260
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, Baocai Yin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25261-25279
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Fourier Learning with Cyclical Data
Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25280-25301
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Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25302-25312
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A Study of Face Obfuscation in ImageNet
Kaiyu Yang, Jacqueline H. Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25313-25330
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Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25331-25363
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Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
Kaiwen Yang, Tianyi Zhou, Xinmei Tian, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25364-25381
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Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25382-25406
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Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25407-25437
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NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Xingcheng Yao, Yanan Zheng, Xiaocong Yang, Zhilin Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25438-25451
Feature Space Particle Inference for Neural Network Ensembles
Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25452-25468
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Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
Mao Ye, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25469-25489
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Be Like Water: Adaptive Floating Point for Machine Learning
Thomas Yeh, Max Sterner, Zerlina Lai, Brandon Chuang, Alexander Ihler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25490-25500
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QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Liping Yi, Wang Gang, Liu Xiaoguang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25501-25513
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De novo mass spectrometry peptide sequencing with a transformer model
Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S Noble; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25514-25522
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Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations
Fan Yin, Jieying Jiao, Jun Yan, Guanyu Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25523-25551
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Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25552-25565
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ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Haoran You, Baopu Li, Shi Huihong, Yonggan Fu, Yingyan Lin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25566-25580
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Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
Zhaoning Yu, Hongyang Gao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25581-25594
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Understanding Robust Overfitting of Adversarial Training and Beyond
Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25595-25610
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How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25611-25635
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Reachability Constrained Reinforcement Learning
Dongjie Yu, Haitong Ma, Shengbo Li, Jianyu Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25636-25655
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
Sixing Yu, Arya Mazaheri, Ali Jannesari; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25656-25667
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The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu, Thiago Serra, Srikumar Ramalingam, Shandian Zhe; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25668-25683
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GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25684-25701
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Latent Diffusion Energy-Based Model for Interpretable Text Modelling
Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25702-25720
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Predicting Out-of-Distribution Error with the Projection Norm
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25721-25746
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Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
Haoqi Yuan, Zongqing Lu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25747-25759
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Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25760-25782
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Neural Tangent Kernel Empowered Federated Learning
Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25783-25803
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Time Is MattEr: Temporal Self-supervision for Video Transformers
Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, Jinwoo Shin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25804-25816
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Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Shiran Zada, Itay Benou, Michal Irani; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25817-25833
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Adaptive Conformal Predictions for Time Series
Margaux Zaffran, Olivier Feron, Yannig Goude, Julie Josse, Aymeric Dieuleveut; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25834-25866
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Actor-Critic based Improper Reinforcement Learning
Mohammadi Zaki, Avi Mohan, Aditya Gopalan, Shie Mannor; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25867-25919
Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning
Andrea Zanette, Martin Wainwright; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25920-25954
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Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25955-25972
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Efficient PAC Learning from the Crowd with Pairwise Comparisons
Shiwei Zeng, Jie Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25973-25993
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Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Yan Zeng, Xinsong Zhang, Hang Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:25994-26009
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Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Y Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua M Susskind; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26010-26027
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Anytime Information Cascade Popularity Prediction via Self-Exciting Processes
Xi Zhang, Akshay Aravamudan, Georgios C Anagnostopoulos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26028-26047
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Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26048-26067
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Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs
Yikang Zhang, Zhuo Chen, Zhao Zhong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26068-26084
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PDE-Based Optimal Strategy for Unconstrained Online Learning
Zhiyu Zhang, Ashok Cutkosky, Ioannis Paschalidis; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26085-26115
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Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26116-26134
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When and How Mixup Improves Calibration
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26135-26160
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UAST: Uncertainty-Aware Siamese Tracking
Dawei Zhang, Yanwei Fu, Zhonglong Zheng; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26161-26175
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Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, Orhan Firat; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26176-26192
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Revisiting End-to-End Speech-to-Text Translation From Scratch
Biao Zhang, Barry Haddow, Rico Sennrich; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26193-26205
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A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Nicola Elia; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26206-26222
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GALAXY: Graph-based Active Learning at the Extreme
Jifan Zhang, Julian Katz-Samuels, Robert Nowak; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26223-26238
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Fairness Interventions as (Dis)Incentives for Strategic Manipulation
Xueru Zhang, Mohammad Mahdi Khalili, Kun Jin, Parinaz Naghizadeh, Mingyan Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26239-26264
Role-based Multiplex Network Embedding
Hegui Zhang, Gang Kou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26265-26280
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Dynamic Topic Models for Temporal Document Networks
Delvin Ce Zhang, Hady Lauw; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26281-26292
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Personalized Federated Learning via Variational Bayesian Inference
Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, Yunfeng Shao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26293-26310
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Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26311-26329
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Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective
Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26330-26346
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26347-26361
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Deep and Flexible Graph Neural Architecture Search
Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, Bin Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26362-26374
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A Langevin-like Sampler for Discrete Distributions
Ruqi Zhang, Xingchao Liu, Qiang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26375-26396
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Rich Feature Construction for the Optimization-Generalization Dilemma
Jianyu Zhang, David Lopez-Paz, Leon Bottou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26397-26411
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Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26412-26428
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Neurotoxin: Durable Backdoors in Federated Learning
Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael Mahoney, Prateek Mittal, Ramchandran Kannan, Joseph Gonzalez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26429-26446
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Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, Bo Dai; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26447-26466
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NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26467-26483
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Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
Michael Zhang, Nimit S Sohoni, Hongyang R Zhang, Chelsea Finn, Christopher Re; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26484-26516
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Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach
Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26517-26547
Partial Counterfactual Identification from Observational and Experimental Data
Junzhe Zhang, Jin Tian, Elias Bareinboim; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26548-26558
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Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
Lily Zhang, Veronica Tozzo, John Higgins, Rajesh Ranganath; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26559-26574
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Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Mingrui Zhang, Jianhong Wang, James B Tlhomole, Matthew Piggott; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26575-26590
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, Zico Kolter; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26591-26604
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A Simple yet Universal Strategy for Online Convex Optimization
Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26605-26623
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Low-Precision Stochastic Gradient Langevin Dynamics
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26624-26644
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Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26645-26654
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Uncertainty Modeling in Generative Compressed Sensing
Yilang Zhang, Mengchu Xu, Xiaojun Mao, Jian Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26655-26668
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26669-26692
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Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26693-26712
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Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni, Mengdi Wang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26713-26749
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ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
Jiayao Zhang, Hongming Zhang, Weijie Su, Dan Roth; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26750-26771
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No-Regret Learning in Time-Varying Zero-Sum Games
Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26772-26808
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PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26809-26823
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NysADMM: faster composite convex optimization via low-rank approximation
Shipu Zhao, Zachary Frangella, Madeleine Udell; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26824-26840
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Toward Compositional Generalization in Object-Oriented World Modeling
Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L.S. Wong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26841-26864
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Dynamic Regret of Online Markov Decision Processes
Peng Zhao, Long-Fei Li, Zhi-Hua Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26865-26894
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Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Qingqing Zhao, David B Lindell, Gordon Wetzstein; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26895-26910
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Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26911-26926
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Global Optimization Networks
Sen Zhao, Erez Louidor, Maya Gupta; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26927-26957
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Certified Robustness Against Natural Language Attacks by Causal Intervention
Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26958-26970
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Efficient Learning for AlphaZero via Path Consistency
Dengwei Zhao, Shikui Tu, Lei Xu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26971-26981
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Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
Yang Zhao, Hao Zhang, Xiuyuan Hu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26982-26992
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Ripple Attention for Visual Perception with Sub-quadratic Complexity
Lin Zheng, Huijie Pan, Lingpeng Kong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26993-27010
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Linear Complexity Randomized Self-attention Mechanism
Lin Zheng, Chong Wang, Lingpeng Kong; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27011-27041
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Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27042-27059
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Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27060-27074
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HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27075-27098
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Describing Differences between Text Distributions with Natural Language
Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27099-27116
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Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27117-27142
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Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization
Dongruo Zhou, Quanquan Gu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27143-27158
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A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
Weichao Zhou, Wenchao Li; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27159-27178
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On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features
Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27179-27202
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27203-27221
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Sparse Invariant Risk Minimization
Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27222-27244
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Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27245-27267
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27268-27286
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Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Zonghao Chen, Tong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27287-27302
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Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Baojian Zhou, Yifan Sun; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27303-27337
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27338-27352
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Modeling Adversarial Noise for Adversarial Training
Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27353-27366
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Contrastive Learning with Boosted Memorization
Zhihan Zhou, Jiangchao Yao, Yan-Feng Wang, Bo Han, Ya Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27367-27377
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Understanding The Robustness in Vision Transformers
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, Jose M. Alvarez; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27378-27394
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VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27395-27411
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Detecting Corrupted Labels Without Training a Model to Predict
Zhaowei Zhu, Zihao Dong, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27412-27427
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Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan J Foster, John Langford, Paul Mineiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27428-27453
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Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27454-27478
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Topology-aware Generalization of Decentralized SGD
Tongtian Zhu, Fengxiang He, Lan Zhang, Zhengyang Niu, Mingli Song, Dacheng Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27479-27503
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Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
Zhuangdi Zhu, Junyuan Hong, Steve Drew, Jiayu Zhou; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27504-27526
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On Numerical Integration in Neural Ordinary Differential Equations
Aiqing Zhu, Pengzhan Jin, Beibei Zhu, Yifa Tang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27527-27547
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27548-27573
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Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
Yinglun Zhu, Paul Mineiro; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27574-27590
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Residual-Based Sampling for Online Outlier-Robust PCA
Tianhao Zhu, Jie Shen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27591-27611
Region-Based Semantic Factorization in GANs
Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27612-27632
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Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu, Jialu Wang, Yang Liu; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27633-27653
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Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Brian Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27654-27670
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm
Pini Zilber, Boaz Nadler; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27671-27692
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Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27693-27706
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
Xinrui Zu, Qian Tao; Proceedings of the 39th International Conference on Machine Learning, PMLR 162:27707-27723
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