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Editors: Hal Daumé III, Aarti Singh
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Selective Dyna-Style Planning Under Limited Model Capacity
; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1-10
A distributional view on multi-objective policy optimization
Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11-22
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille, Alessandro Lazaric; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:23-31
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, Thomas Moreau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:32-41
A Geometric Approach to Archetypal Analysis via Sparse Projections
Vinayak Abrol, Pulkit Sharma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:42-51
Context Aware Local Differential Privacy
Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:52-62
Efficient Intervention Design for Causal Discovery with Latents
Raghavendra Addanki, Shiva Kasiviswanathan, Andrew Mcgregor, Cameron Musco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:63-73
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam, Jeffrey Pennington; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:74-84
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:85-95
Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:96-103
An Optimistic Perspective on Offline Reinforcement Learning
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:104-114
Optimal Bounds between f-Divergences and Integral Probability Metrics
Rohit Agrawal, Thibaut Horel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:115-124
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LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
Ali Ahmaditeshnizi, Saber Salehkaleybar, Negar Kiyavash; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:125-133
Learning What to Defer for Maximum Independent Sets
Sungsoo Ahn, Younggyo Seo, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:134-144
Invariant Risk Minimization Games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, Amit Dhurandhar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:145-155
Why bigger is not always better: on finite and infinite neural networks
Laurence Aitchison; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:156-164
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Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:165-174
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:175-190
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:191-201
A new regret analysis for Adam-type algorithms
Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:202-210
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Reda Alami, Odalric Maillard, Raphael Feraud; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:211-221
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Amr Alexandari, Anshul Kundaje, Avanti Shrikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:222-232
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali, Edgar Dobriban, Ryan Tibshirani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:233-244
Structural Language Models of Code
Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:245-256
LowFER: Low-rank Bilinear Pooling for Link Prediction
Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:257-268
Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit, Ron Meir, Kamil Ciosek; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:269-278
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:279-290
The Differentiable Cross-Entropy Method
Brandon Amos, Denis Yarats; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:291-302
Customizing ML Predictions for Online Algorithms
Keerti Anand, Rong Ge, Debmalya Panigrahi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:303-313
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Fairwashing explanations with off-manifold detergent
Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:314-323
Population-Based Black-Box Optimization for Biological Sequence Design
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:324-334
Low-loss connection of weight vectors: distribution-based approaches
Ivan Anokhin, Dmitry Yarotsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:335-344
Online metric algorithms with untrusted predictions
Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:345-355
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:356-366
Provable Representation Learning for Imitation Learning via Bi-level Optimization
Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, Nikunj Saunshi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:367-376
Quantum Boosting
Srinivasan Arunachalam, Reevu Maity; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:377-387
Black-box Certification and Learning under Adversarial Perturbations
Hassan Ashtiani, Vinayak Pathak, Ruth Urner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:388-398
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong, Ali Ahmed, Paul Hand; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:399-409
On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings
Mahmoud Assran, Mike Rabbat; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:410-420
Safe screening rules for L0-regression from Perspective Relaxations
Alper Atamturk, Andres Gomez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:421-430
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi, Natalie Frank, Mehryar Mohri; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:431-441
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:442-451
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Kyriakos Axiotis, Maxim Sviridenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:452-462
Model-Based Reinforcement Learning with Value-Targeted Regression
Alex Ayoub, Zeyu Jia, Csaba Szepesvari, Mengdi Wang, Lin Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:463-474
Forecasting Sequential Data Using Consistent Koopman Autoencoders
Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:475-485
Constant Curvature Graph Convolutional Networks
Gregor Bachmann, Gary Becigneul, Octavian Ganea; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:486-496
Scalable Nearest Neighbor Search for Optimal Transport
Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:497-506
Agent57: Outperforming the Atari Human Benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:507-517
Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:518-527
Learning De-biased Representations with Biased Representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:528-539
Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:540-550
Provable Self-Play Algorithms for Competitive Reinforcement Learning
Yu Bai, Chi Jin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:551-560
Sparse Subspace Clustering with Entropy-Norm
Liang Bai, Jiye Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:561-568
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Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:569-579
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:580-590
Ready Policy One: World Building Through Active Learning
Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:591-601
Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu, Quentin Berthet, Francis Bach; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:602-612
Dual Mirror Descent for Online Allocation Problems
Santiago Balseiro, Haihao Lu, Vahab Mirrokni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:613-628
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Subho Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar Iyer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:629-641
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:642-652
Fast OSCAR and OWL Regression via Safe Screening Rules
Runxue Bao, Bin Gu, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:653-663
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Option Discovery in the Absence of Rewards with Manifold Analysis
Amitay Bar, Ronen Talmon, Ron Meir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:664-674
Learning the piece-wise constant graph structure of a varying Ising model
Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:675-684
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Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri, Meirav Galun, Amnon Geifman, David Jacobs, Yoni Kasten, Shira Kritchman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:685-694
Private Query Release Assisted by Public Data
Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:695-703
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ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:704-714
On Second-Order Group Influence Functions for Black-Box Predictions
Samyadeep Basu, Xuchen You, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:715-724
Kernel interpolation with continuous volume sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:725-735
Decoupled Greedy Learning of CNNs
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:736-745
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
Pierre Bellec, Dana Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:746-755
Defense Through Diverse Directions
Christopher Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:756-766
Interference and Generalization in Temporal Difference Learning
Emmanuel Bengio, Joelle Pineau, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:767-777
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett, Nathan Kallus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:788-798
Training Neural Networks for and by Interpolation
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:799-809
Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:810-821
Online Learning with Imperfect Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:822-831
When are Non-Parametric Methods Robust?
Robi Bhattacharjee, Kamalika Chaudhuri; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:832-841
Learning and Sampling of Atomic Interventions from Observations
Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:842-853
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Chiranjib Bhattacharyya, Ravindran Kannan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:854-863
Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:864-873
Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:874-883
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Ioana Bica, Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:884-895
The Boomerang Sampler
Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:908-918
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau, Dylan Foster, Daniel Roy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:919-929
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Ilai Bistritz, Tavor Baharav, Amir Leshem, Nicholas Bambos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:930-940
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Provable guarantees for decision tree induction: the agnostic setting
Guy Blanc, Jane Lange, Li-Yang Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:941-949
Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:950-959
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:960-969
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill Campbell, Carl Henrik Ek; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:970-979
Deep Coordination Graphs
Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:980-991
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy, Brandon Anderson, Jan Offermann, Marwah Roussi, David Miller, Risi Kondor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:992-1002
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1003-1013
Proper Network Interpretability Helps Adversarial Robustness in Classification
Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1014-1023
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1024-1034
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Jorg Bornschein, Francesco Visin, Simon Osindero; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1035-1044
Latent Variable Modelling with Hyperbolic Normalizing Flows
Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, Will Hamilton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1045-1055
Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1056-1066
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1067-1077
Adversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, Yejin Choi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1078-1088
Calibration, Entropy Rates, and Memory in Language Models
Mark Braverman, Xinyi Chen, Sham Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1089-1099
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1100-1110
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1111-1122
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Estimating the Number and Effect Sizes of Non-null Hypotheses
Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1123-1133
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1144-1152
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TaskNorm: Rethinking Batch Normalization for Meta-Learning
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard Turner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1153-1164
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1165-1177
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1178-1189
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel Bruinsma, Eric Perim, William Tebbutt, Scott Hosking, Arno Solin, Richard Turner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1190-1201
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Jinzhi Bu, David Simchi-Levi, Yunzong Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1202-1210
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Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1211-1219
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl, Tijl De Bie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1220-1229
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Structured Prediction with Partial Labelling through the Infimum Loss
Vivien Cabannnes, Alessandro Rudi, Francis Bach; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1230-1239
Online Learned Continual Compression with Adaptive Quantization Modules
Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1240-1250
Boosted Histogram Transform for Regression
Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1251-1261
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Hengrui Cai, Wenbin Lu, Rui Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1262-1270
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Changxiao Cai, H. Vincent Poor, Yuxin Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1271-1282
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Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1283-1294
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1295-1305
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1306-1316
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-I-Nieto, Jordi Torres; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1317-1327
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Asaf Cassel, Alon Cohen, Tomer Koren; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1328-1337
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jeremy Rapin, Morgane Riviere, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1338-1348
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Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, Nisheeth Vishnoi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1349-1359
Meta-learning with Stochastic Linear Bandits
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1360-1370
Description Based Text Classification with Reinforcement Learning
Duo Chai, Wei Wu, Qinghong Han, Fei Wu, Jiwei Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1371-1382
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1383-1391
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex Chan, Ahmed Alaa, Zhaozhi Qian, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1392-1402
Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1403-1413
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Optimizing for the Future in Non-Stationary MDPs
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1414-1425
Learning to Simulate and Design for Structural Engineering
Kai-Hung Chang, Chin-Yi Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1426-1436
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Michael Chang, Sid Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1437-1447
Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1448-1458
Circuit-Based Intrinsic Methods to Detect Overfitting
Satrajit Chatterjee, Alan Mishchenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1459-1468
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Better depth-width trade-offs for neural networks through the lens of dynamical systems
Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1469-1478
Explainable and Discourse Topic-aware Neural Language Understanding
Yatin Chaudhary, Hinrich Schuetze, Pankaj Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1479-1488
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Lakshay Chauhan, John Alberg, Zachary Lipton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1489-1499
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John Gregoire, Carla Gomes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1500-1509
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Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1510-1519
Learning To Stop While Learning To Predict
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1520-1530
Combinatorial Pure Exploration for Dueling Bandit
Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1531-1541
Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1542-1553
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Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Xiangning Chen, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1554-1565
Mapping natural-language problems to formal-language solutions using structured neural representations
Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Ken Forbus, Jianfeng Gao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1566-1575
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1576-1586
Learning Flat Latent Manifolds with VAEs
Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick Van Der Smagt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1587-1596
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1597-1607
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Binghong Chen, Chengtao Li, Hanjun Dai, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1608-1616
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen, Lala Li, Yizhou Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1617-1626
On Efficient Constructions of Checkpoints
Yu Chen, Zhenming Liu, Bin Ren, Xin Jin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1627-1636
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Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1637-1648
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Jessie X.T. Chen, Miles Lopes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1649-1659
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VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1660-1669
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1670-1680
An Accelerated DFO Algorithm for Finite-sum Convex Functions
Yuwen Chen, Antonio Orvieto, Aurelien Lucchi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1681-1690
Generative Pretraining From Pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1691-1703
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Negative Sampling in Semi-Supervised learning
John Chen, Vatsal Shah, Anastasios Kyrillidis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1704-1714
Optimization from Structured Samples for Coverage Functions
Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1715-1724
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Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1725-1735
On Breaking Deep Generative Model-based Defenses and Beyond
Yanzhi Chen, Renjie Xie, Zhanxing Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1736-1745
Automated Synthetic-to-Real Generalization
Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1746-1756
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(Locally) Differentially Private Combinatorial Semi-Bandits
Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1757-1767
High-dimensional Robust Mean Estimation via Gradient Descent
Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1768-1778
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1779-1788
Learning with Bounded Instance and Label-dependent Label Noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1789-1799
Mutual Transfer Learning for Massive Data
Ching-Wei Cheng, Xingye Qiao, Guang Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1800-1809
Stochastic Gradient and Langevin Processes
Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1810-1819
Representation Learning via Adversarially-Contrastive Optimal Transport
Anoop Cherian, Shuchin Aeron; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1820-1830
Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1831-1842
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1843-1854
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Streaming Coresets for Symmetric Tensor Factorization
Rachit Chhaya, Jayesh Choudhari, Anirban Dasgupta, Supratim Shit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1855-1865
On Coresets for Regularized Regression
Rachit Chhaya, Anirban Dasgupta, Supratim Shit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1866-1876
How to Solve Fair k-Center in Massive Data Models
Ashish Chiplunkar, Sagar Kale, Sivaramakrishnan Natarajan Ramamoorthy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1877-1886
Fair Generative Modeling via Weak Supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1887-1898
Encoding Musical Style with Transformer Autoencoders
Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1899-1908
k-means++: few more steps yield constant approximation
Davin Choo, Christoph Grunau, Julian Portmann, Vaclav Rozhon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1909-1917
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1918-1928
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1929-1938
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1939-1951
Online Continual Learning from Imbalanced Data
Aristotelis Chrysakis, Marie-Francine Moens; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1952-1961
Distance Metric Learning with Joint Representation Diversification
Xu Chu, Yang Lin, Yasha Wang, Xiting Wang, Hailong Yu, Xin Gao, Qi Tong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1962-1973
Semismooth Newton Algorithm for Efficient Projections onto $\ell_1, ∞$-norm Ball
Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1974-1983
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1984-1994
Scalable and Efficient Comparison-based Search without Features
Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1995-2005
Feature-map-level Online Adversarial Knowledge Distillation
Inseop Chung, Seonguk Park, Jangho Kim, Nojun Kwak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2006-2015
Teaching with Limited Information on the Learner’s Behaviour
Ferdinando Cicalese, Sergio Filho, Eduardo Laber, Marco Molinaro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2016-2026
Deep Divergence Learning
Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2027-2037
Model Fusion with Kullback-Leibler Divergence
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2038-2047
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Chris Hesse, Jacob Hilton, John Schulman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2048-2056
Composable Sketches for Functions of Frequencies: Beyond the Worst Case
Edith Cohen, Ofir Geri, Rasmus Pagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2057-2067
Healing Products of Gaussian Process Experts
Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2068-2077
On Efficient Low Distortion Ultrametric Embedding
Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2078-2088
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Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
Benjamin Coleman, Richard Baraniuk, Anshumali Shrivastava; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2089-2099
Word-Level Speech Recognition With a Letter to Word Encoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2100-2110
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Boosting Frank-Wolfe by Chasing Gradients
Cyrille Combettes, Sebastian Pokutta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2111-2121
Learning Opinions in Social Networks
Vincent Conitzer, Debmalya Panigrahi, Hanrui Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2122-2132
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Rob Cornish, Anthony Caterini, George Deligiannidis, Arnaud Doucet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2133-2143
Adaptive Region-Based Active Learning
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2144-2153
Online Learning with Dependent Stochastic Feedback Graphs
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2154-2163
Learnable Group Transform For Time-Series
Romain Cosentino, Behnaam Aazhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2164-2173
Causal Modeling for Fairness In Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2185-2195
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce, Matthias Hein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2196-2205
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce, Matthias Hein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2206-2216
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant, Marc Abeille, Clement Calauzenes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2217-2226
Privately detecting changes in unknown distributions
Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2227-2237
Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2238-2249
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning
Ashok Cutkosky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2250-2259
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Supervised Quantile Normalization for Low Rank Matrix Factorization
Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2269-2279
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime
Stéphane D’Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2280-2290
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Teck-Hua Ho; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2291-2301
Scalable Deep Generative Modeling for Sparse Graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2302-2312
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2313-2322
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Niccolo Dalmasso, Rafael Izbicki, Ann Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2323-2334
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Soham Dan, Bhaswar B. Bhattacharya; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2335-2344
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
Chen Dan, Yuting Wei, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2345-2355
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2356-2365
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard Baraniuk; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2366-2375
Probing Emergent Semantics in Predictive Agents via Question Answering
Abhishek Das, Federico Carnevale, Hamza Merzic, Laura Rimell, Rosalia Schneider, Josh Abramson, Alden Hung, Arun Ahuja, Stephen Clark, Greg Wayne, Felix Hill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2376-2391
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Trevor Davis, Martin Schmid, Michael Bowling; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2392-2401
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Filipe De Avila Belbute-Peres, Thomas Economon, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2402-2411
Representing Unordered Data Using Complex-Weighted Multiset Automata
Justin DeBenedetto, David Chiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2412-2420
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2421-2431
Gamification of Pure Exploration for Linear Bandits
Rémy Degenne, Pierre Menard, Xuedong Shang, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2432-2442
Structure Adaptive Algorithms for Stochastic Bandits
Rémy Degenne, Han Shao, Wouter Koolen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2443-2452
Randomly Projected Additive Gaussian Processes for Regression
Ian Delbridge, David Bindel, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2453-2463
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2464-2473
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2474-2483
Towards Understanding the Dynamics of the First-Order Adversaries
Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie Su; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2484-2493
Robust Pricing in Dynamic Mechanism Design
Yuan Deng, Sebastien Lahaie, Vahab Mirrokni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2494-2503
A Swiss Army Knife for Minimax Optimal Transport
Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2504-2513
Margin-aware Adversarial Domain Adaptation with Optimal Transport
Sofien Dhouib, Ievgen Redko, Carole Lartizien; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2514-2524
Enhancing Simple Models by Exploiting What They Already Know
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2525-2534
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun Ding, Yingjie Fei, Qiantong Xu, Chengrun Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2535-2544
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Liang Ding, Rui Tuo, Shahin Shahrampour; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2545-2555
Layered Sampling for Robust Optimization Problems
Hu Ding, Zixiu Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2556-2566
Growing Adaptive Multi-hyperplane Machines
Nemanja Djuric, Zhuang Wang, Slobodan Vucetic; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2567-2576
Inexact Tensor Methods with Dynamic Accuracies
Nikita Doikov, Yurii Nesterov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2577-2586
Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2587-2596
Optimal Differential Privacy Composition for Exponential Mechanisms
Jinshuo Dong, David Durfee, Ryan Rogers; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2597-2606
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2607-2615
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Chengyu Dong, Liyuan Liu, Zichao Li, Jingbo Shang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2616-2626
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2627-2637
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Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong, Bryan Seybold, Kevin Murphy, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2638-2647
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Chaosheng Dong, Bo Zeng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2648-2657
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
Yoel Drori, Ohad Shamir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2658-2667
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
Alexey Drutsa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2668-2677
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Alexey Drutsa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2678-2689
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Tony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Ng, Alejandro Schuler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2690-2700
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Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Yaqi Duan, Zeyu Jia, Mengdi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2701-2709
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Online Bayesian Moment Matching based SAT Solver Heuristics
Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2710-2719
Familywise Error Rate Control by Interactive Unmasking
Boyan Duan, Aaditya Ramdas, Larry Wasserman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2720-2729
Cooperative Multi-Agent Bandits with Heavy Tails
Abhimanyu Dubey, Alex ‘Sandy’ Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2730-2739
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Abhimanyu Dubey, Alex ‘Sandy’ Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2740-2750
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
Yonatan Dukler, Quanquan Gu, Guido Montufar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2751-2760
Equivariant Neural Rendering
Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Josh Susskind, Qi Shan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2761-2770
On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2771-2781
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2782-2792
Sparse Gaussian Processes with Spherical Harmonic Features
Vincent Dutordoir, Nicolas Durrande, James Hensman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2793-2802
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush Varshney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2803-2813
Self-Concordant Analysis of Frank-Wolfe Algorithms
Pavel Dvurechensky, Petr Ostroukhov, Kamil Safin, Shimrit Shtern, Mathias Staudigl; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2814-2824
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients
Ashley Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2825-2835
Training Linear Neural Networks: Non-Local Convergence and Complexity Results
Armin Eftekhari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2836-2847
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed El-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2848-2857
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan Mcnellis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2858-2867
Revisiting Spatial Invariance with Low-Rank Local Connectivity
Gamaleldin Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2868-2879
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2880-2891
Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson Fletcher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2892-2901
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Alina Ene, Huy Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2902-2911
Continuous Time Bayesian Networks with Clocks
Nicolai Engelmann, Dominik Linzner, Heinz Koeppl; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2912-2921
Identifying Statistical Bias in Dataset Replication
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2922-2932
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent
Nima Eshraghi, Ben Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2933-2942
Rigging the Lottery: Making All Tickets Winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2943-2952
Faster Graph Embeddings via Coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2953-2963
Latent Bernoulli Autoencoder
Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2964-2974
Optimal Sequential Maximization: One Interview is Enough!
Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2975-2984
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2985-2995
On hyperparameter tuning in general clustering problemsm
Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2996-3007
Online mirror descent and dual averaging: keeping pace in the dynamic case
Huang Fang, Nick Harvey, Victor Portella, Michael Friedlander; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3008-3017
Stochastic Regret Minimization in Extensive-Form Games
Gabriele Farina, Christian Kroer, Tuomas Sandholm; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3018-3028
Do GANs always have Nash equilibria?
Farzan Farnia, Asuman Ozdaglar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3029-3039
Growing Action Spaces
Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3040-3051
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Clement Calauzenes, Olivier Fercoq; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3052-3060
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3061-3071
Learning with Multiple Complementary Labels
Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3072-3081
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3082-3091
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
Zhe Feng, David Parkes, Haifeng Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3092-3101
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3102-3111
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3112-3122
Why Are Learned Indexes So Effective?
Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3123-3132
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Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
Tanner Fiez, Benjamin Chasnov, Lillian Ratliff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3133-3144
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigkas, Rowan Mcallister, Nicholas Rhinehart, Sergey Levine, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3145-3153
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay, Joern-Henrik Jacobsen, Levon Nurbekyan, Adam Oberman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3154-3164
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3165-3176
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Johannes Fischer, Ömer Sahin Tas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3177-3187
Topic Modeling via Full Dependence Mixtures
Dan Fisher, Mark Kozdoba, Shie Mannor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3188-3198
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
Dylan Foster, Alexander Rakhlin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3199-3210
Logarithmic Regret for Adversarial Online Control
Dylan Foster, Max Simchowitz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3211-3221
p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis, Di Wang, Shenghao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3222-3232
Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickael Chen, Sylvain Lamprier, Patrick Gallinari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3233-3246
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Leveraging Frequency Analysis for Deep Fake Image Recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3247-3258
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3259-3269
No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David Pennock, Chara Podimata, Jennifer Wortman Vaughan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3270-3279
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Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, Christopher Re; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3280-3291
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3292-3303
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Don’t Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3304-3314
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3315-3326
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions
Kaito Fujii; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3327-3336
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3337-3347
Stochastic bandits with arm-dependent delays
Manegueu Anne Gael, Claire Vernade, Alexandra Carpentier, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3348-3356
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
Alex Gain, Hava Siegelmann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3357-3366
A Free-Energy Principle for Representation Learning
Yansong Gao, Pratik Chaudhari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3367-3376
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
Hongchang Gao, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3377-3386
Online Convex Optimization in the Random Order Model
Dan Garber, Gal Korcia, Kfir Levy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3387-3396
Symbolic Network: Generalized Neural Policies for Relational MDPs
Sankalp Garg, Aniket Bajpai, Mausam ; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3397-3407
Predicting deliberative outcomes
Vikas Garg, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3408-3418
Generalization and Representational Limits of Graph Neural Networks
Vikas Garg, Stefanie Jegelka, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3419-3430
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
Sinong Geng, Houssam Nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3431-3441
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3442-3451
Generalisation error in learning with random features and the hidden manifold model
Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mezard, Lenka Zdeborova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3452-3462
Black-Box Methods for Restoring Monotonicity
Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3463-3473
Online Multi-Kernel Learning with Graph-Structured Feedback
Pouya M Ghari, Yanning Shen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3474-3483
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Mahsa Ghasemi, Erdem Bulgur, Ufuk Topcu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3484-3493
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Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
Amiremad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3494-3504
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3505-3514
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3515-3523
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Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3524-3534
A Distributional Framework For Data Valuation
Amirata Ghorbani, Michael Kim, James Zou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3535-3544
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Subhroshekhar Ghosh, Krishna Balasubramanian, Xiaochuan Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3545-3555
Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh, Marc G. Bellemare; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3556-3565
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
Alex Gittens, Kareem Aggour, Bülent Yener; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3566-3575
One Size Fits All: Can We Train One Denoiser for All Noise Levels?
Abhiram Gnanasambandam, Stanley Chan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3576-3586
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Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3587-3596
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Tomer Golany, Kira Radinsky, Daniel Freedman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3597-3606
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3607-3616
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Eugene Golikov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3617-3626
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3627-3636
The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John Cunningham; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3637-3647
Automatic Reparameterisation of Probabilistic Programs
Maria Gorinova, Dave Moore, Matthew Hoffman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3648-3657
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, Finale Doshi-Velez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3658-3667
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3668-3679
Ordinal Non-negative Matrix Factorization for Recommendation
Olivier Gouvert, Thomas Oberlin, Cédric Févotte; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3680-3689
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PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal, Ashish Verma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3690-3699
PackIt: A Virtual Environment for Geometric Planning
Ankit Goyal, Jia Deng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3700-3710
DROCC: Deep Robust One-Class Classification
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3711-3721
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3722-3731
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3732-3747
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On the Iteration Complexity of Hypergradient Computation
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3748-3758
Robust Learning with the Hilbert-Schmidt Independence Criterion
Daniel Greenfeld, Uri Shalit; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3759-3768
Monte-Carlo Tree Search as Regularized Policy Optimization
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Remi Munos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3769-3778
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Allan Grønlund, Lior Kamma, Kasper Green Larsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3779-3788
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Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3789-3799
Improving the Gating Mechanism of Recurrent Neural Networks
Albert Gu, Caglar Gulcehre, Thomas Paine, Matt Hoffman, Razvan Pascanu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3800-3809
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3810-3821
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3822-3831
Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun, Laurens Van Der Maaten; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3832-3842
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LTF: A Label Transformation Framework for Correcting Label Shift
Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3843-3853
Learning to Branch for Multi-Task Learning
Pengsheng Guo, Chen-Yu Lee, Daniel Ulbricht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3854-3863
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3864-3874
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Remi Munos, Mohammad Gheshlaghi Azar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3875-3886
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3887-3896
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, Zhi-Hua Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3897-3906
Neural Topic Modeling with Continual Lifelong Learning
Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schuetze; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3907-3917
Multidimensional Shape Constraints
Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3918-3928
Retrieval Augmented Language Model Pre-Training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Mingwei Chang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3929-3938
Streaming Submodular Maximization under a k-Set System Constraint
Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3939-3949
Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Guy Hacohen, Leshem Choshen, Daphna Weinshall; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3950-3960
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Optimal approximation for unconstrained non-submodular minimization
Marwa El Halabi, Stefanie Jegelka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3961-3972
FedBoost: A Communication-Efficient Algorithm for Federated Learning
Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3973-3983
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Insu Han, Haim Avron, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3984-3993
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DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3994-4005
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4006-4016
Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4017-4026
Stochastic Subspace Cubic Newton Method
Filip Hanzely, Nikita Doikov, Yurii Nesterov, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4027-4038
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely, Dmitry Kovalev, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4039-4048
Data Amplification: Instance-Optimal Property Estimation
Yi Hao, Alon Orlitsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4049-4059
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4060-4070
Improving generalization by controlling label-noise information in neural network weights
Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4071-4081
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4082-4093
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4094-4104
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4105-4115
Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani, Amir Hosein Khasahmadi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4116-4126
Nested Subspace Arrangement for Representation of Relational Data
Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4127-4137
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The Tree Ensemble Layer: Differentiability meets Conditional Computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4138-4148
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation
Reinhard Heckel, Mahdi Soltanolkotabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4149-4158
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Hierarchically Decoupled Imitation For Morphological Transfer
Donald Hejna, Lerrel Pinto, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4159-4171
Gradient-free Online Learning in Continuous Games with Delayed Rewards
Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4172-4181
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier Henaff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4182-4192
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
Julien Hendrickx, Alex Olshevsky, Venkatesh Saligrama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4193-4202
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4203-4227
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4228-4238
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4239-4248
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Fabian Hinder, André Artelt, Barbara Hammer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4249-4259
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4260-4270
Optimizing Dynamic Structures with Bayesian Generative Search
Minh Hoang, Carleton Kingsford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4271-4281
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Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4282-4292
Parameterized Rate-Distortion Stochastic Encoder
Quan Hoang, Trung Le, Dinh Phung; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4293-4303
Topologically Densified Distributions
Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4304-4313
Graph Filtration Learning
Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4314-4323
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matthew Hoffman, Yian Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4324-4341
Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4342-4352
Set Functions for Time Series
Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4353-4363
Lifted Disjoint Paths with Application in Multiple Object Tracking
Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4364-4375
Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4376-4386
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip Gibbons; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4387-4398
“Other-Play” for Zero-Shot Coordination
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4399-4410
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XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4411-4421
From Importance Sampling to Doubly Robust Policy Gradient
Jiawei Huang, Nan Jiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4434-4443
Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4444-4454
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4455-4464
Communication-Efficient Distributed PCA by Riemannian Optimization
Long-Kai Huang, Sinno Pan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4465-4474
Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang, Felipe Perez, Jimmy Ba, Maksims Volkovs; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4475-4483
More Information Supervised Probabilistic Deep Face Embedding Learning
Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4484-4494
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Generating Programmatic Referring Expressions via Program Synthesis
Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4495-4506
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InstaHide: Instance-hiding Schemes for Private Distributed Learning
Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4507-4518
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4531-4541
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy
Jiaoyang Huang, Horng-Tzer Yau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4542-4551
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Curvature-corrected learning dynamics in deep neural networks
Dongsung Huh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4552-4560
Multigrid Neural Memory
Tri Huynh, Michael Maire, Matthew Walter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4561-4571
Meta-Learning with Shared Amortized Variational Inference
Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4572-4582
Linear Lower Bounds and Conditioning of Differentiable Games
Adam Ibrahim, Waı̈ss Azizian, Gauthier Gidel, Ioannis Mitliagkas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4583-4593
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4594-4603
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4604-4614
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4615-4630
Implicit Regularization of Random Feature Models
Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4631-4640
Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4641-4650
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Optimal Robust Learning of Discrete Distributions from Batches
Ayush Jain, Alon Orlitsky; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4651-4660
Generalization to New Actions in Reinforcement Learning
Ayush Jain, Andrew Szot, Joseph Lim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4661-4672
Tails of Lipschitz Triangular Flows
Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4673-4681
Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4682-4691
Debiased Sinkhorn barycenters
Hicham Janati, Marco Cuturi, Alexandre Gramfort; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4692-4701
Parametric Gaussian Process Regressors
Martin Jankowiak, Geoff Pleiss, Jacob Gardner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4702-4712
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Daniel Jarrett, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4713-4723
Extra-gradient with player sampling for faster convergence in n-player games
Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4736-4745
T-GD: Transferable GAN-generated Images Detection Framework
Hyeonseong Jeon, Young Oh Bang, Junyaup Kim, Simon Woo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4746-4761
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History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4762-4772
Information-Theoretic Local Minima Characterization and Regularization
Zhiwei Jia, Hao Su; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4773-4783
Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4784-4793
BINOCULARS for efficient, nonmyopic sequential experimental design
Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4794-4803
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Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang, Di Huang, Mason Liu, Weilong Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4804-4815
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4816-4827
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Yibo Jiang, Cengiz Pehlevan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4828-4838
Hierarchical Generation of Molecular Graphs using Structural Motifs
Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4839-4848
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Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4849-4859
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Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4860-4869
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4870-4879
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Chi Jin, Praneeth Netrapalli, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4880-4889
Efficiently Solving MDPs with Stochastic Mirror Descent
Yujia Jin, Aaron Sidford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4890-4900
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Ying Jin, Zhaoran Wang, Junwei Lu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4901-4910
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Tyler Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4911-4920
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Rie Johnson, Tong Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4921-4930
Fair k-Centers via Maximum Matching
Matthew Jones, Huy Nguyen, Thy Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4940-4949
Being Bayesian about Categorical Probability
Taejong Joo, Uijung Chung, Min-Gwan Seo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4950-4961
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Evaluating the Performance of Reinforcement Learning Algorithms
Scott Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4962-4973
Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4974-4983
A simpler approach to accelerated optimization: iterative averaging meets optimism
Pooria Joulani, Anant Raj, Andras Gyorgy, Csaba Szepesvari; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4984-4993
Sets Clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:4994-5005
Distribution Augmentation for Generative Modeling
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5006-5019
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Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5020-5030
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri, Stephane Ayache, Riikka Huusari, Alain Rakotomamonjy, Ralaivola Liva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5031-5041
Strategyproof Mean Estimation from Multiple-Choice Questions
Anson Kahng, Gregory Kehne, Ariel Procaccia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5042-5052
Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Soren Hauberg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5053-5066
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
Nathan Kallus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5067-5077
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation
Nathan Kallus, Masatoshi Uehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5078-5088
Statistically Efficient Off-Policy Policy Gradients
Nathan Kallus, Masatoshi Uehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5089-5100
On the Power of Compressed Sensing with Generative Models
Akshay Kamath, Eric Price, Sushrut Karmalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5101-5109
Learning and Evaluating Contextual Embedding of Source Code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5110-5121
Operation-Aware Soft Channel Pruning using Differentiable Masks
Minsoo Kang, Bohyung Han; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5122-5131
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, Ananda Theertha Suresh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5132-5143
Non-autoregressive Machine Translation with Disentangled Context Transformer
Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5144-5155
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Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5156-5165
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space
Keizo Kato, Jing Zhou, Tomotake Sasaki, Akira Nakagawa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5166-5176
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Stephen Keeley, David Zoltowski, Yiyi Yu, Spencer Smith, Jonathan Pillow; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5177-5186
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Quantum Expectation-Maximization for Gaussian mixture models
Iordanis Kerenidis, Alessandro Luongo, Anupam Prakash; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5187-5197
Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5198-5208
Feature Noise Induces Loss Discrepancy Across Groups
Fereshte Khani, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5209-5219
Entropy Minimization In Emergent Languages
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5220-5230
Private Outsourced Bayesian Optimization
Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5231-5242
What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5243-5253
Uniform Convergence of Rank-weighted Learning
Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5254-5263
FACT: A Diagnostic for Group Fairness Trade-offs
Joon Sik Kim, Jiahao Chen, Ameet Talwalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5264-5274
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5275-5285
Domain Adaptive Imitation Learning
Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5286-5295
Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5296-5305
Active World Model Learning with Progress Curiosity
Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5306-5315
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse, Michael U. Gutmann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5316-5326
Optimal Continual Learning has Perfect Memory and is NP-hard
Jeremias Knoblauch, Hisham Husain, Tom Diethe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5327-5337
Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5338-5348
Learning Similarity Metrics for Numerical Simulations
Georg Kohl, Kiwon Um, Nils Thuerey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5349-5360
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5361-5370
Online Learning for Active Cache Synchronization
Andrey Kolobov, Sebastien Bubeck, Julian Zimmert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5371-5380
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian Stich; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5381-5393
Meta-learning for Mixed Linear Regression
Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5394-5404
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5405-5415
On the Sample Complexity of Adversarial Multi-Source PAC Learning
Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5416-5425
Asynchronous Coagent Networks
James Kostas, Chris Nota, Philip Thomas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5426-5435
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5436-5446
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Anurag Kumar, Vamsi Ithapu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5447-5457
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5458-5467
Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar, Tengyu Ma, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5468-5479
On Implicit Regularization in $β$-VAEs
Abhishek Kumar, Ben Poole; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5480-5490
Problems with Shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, Sorelle Friedler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5491-5500
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Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Daniel Kumor, Carlos Cinelli, Elias Bareinboim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5501-5510
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Two Routes to Scalable Credit Assignment without Weight Symmetry
Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena, Surya Ganguli, Jonathan Bloom, Daniel Yamins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5511-5521
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5522-5532
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, Dan Alistarh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5533-5543
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Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, Ali Farhadi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5544-5555
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5556-5566
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5567-5576
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Prashanth L.A., Krishna Jagannathan, Ravi Kolla; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5577-5586
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Optimal Randomized First-Order Methods for Least-Squares Problems
Jonathan Lacotte, Mert Pilanci; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5587-5597
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence D’Alché-Buc; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5598-5607
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Zehua Lai, Lek-Heng Lim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5608-5617
Bidirectional Model-based Policy Optimization
Hang Lai, Jian Shen, Weinan Zhang, Yong Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5618-5627
Robust and Stable Black Box Explanations
Himabindu Lakkaraju, Nino Arsov, Osbert Bastani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5628-5638
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Michael Laskin, Aravind Srinivas, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5639-5650
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5651-5661
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Tor Lattimore, Csaba Szepesvari, Gellert Weisz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5662-5670
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Hien Le, Nicolas Gillis, Panagiotis Patrinos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5671-5681
Self-Attentive Associative Memory
Hung Le, Truyen Tran, Svetha Venkatesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5682-5691
Causal Effect Identifiability under Partial-Observability
Sanghack Lee, Elias Bareinboim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5692-5701
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Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5702-5713
Self-supervised Label Augmentation via Input Transformations
Hankook Lee, Sung Ju Hwang, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5714-5724
Batch Reinforcement Learning with Hyperparameter Gradients
Byungjun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5725-5735
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5736-5746
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
Sang-Hyun Lee, Seung-Woo Seo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5747-5756
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5757-5766
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Changhee Lee, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5767-5777
Tensor denoising and completion based on ordinal observations
Chanwoo Lee, Miaoyan Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5778-5788
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Jiabao Lei, Kui Jia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5789-5798
SGD Learns One-Layer Networks in WGANs
Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5799-5808
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Yunwen Lei, Yiming Ying; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5809-5819
Learning Quadratic Games on Networks
Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5820-5830
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Yang Li, Shoaib Akbar, Junier Oliva; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5831-5841
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
Yu-Sheng Li, Wei-Lin Chiang, Ching-Pei Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5842-5852
Neural Architecture Search in A Proxy Validation Loss Landscape
Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5853-5862
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Shiyu Li, Edward Hanson, Hai Li, Yiran Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5863-5873
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
Mingjie Li, Lingshen He, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5874-5883
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5884-5894
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5895-5904
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Jianing Li, Yanyan Lan, Jiafeng Guo, Xueqi Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5905-5915
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5916-5926
Visual Grounding of Learned Physical Models
Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins, Jiajun Wu, Joshua Tenenbaum, Antonio Torralba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5927-5936
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Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Steven Cheng-Xian Li, Benjamin Marlin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5937-5946
Evolutionary Topology Search for Tensor Network Decomposition
Chao Li, Zhun Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5947-5957
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Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joey Gonzalez; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5958-5968
Almost Tune-Free Variance Reduction
Bingcong Li, Lingda Wang, Georgios B. Giannakis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5969-5978
Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5979-5989
Temporal Logic Point Processes
Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:5990-6000
Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li, David Woodruff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6001-6009
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6010-6019
On a projective ensemble approach to two sample test for equality of distributions
Zhimei Li, Yaowu Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6020-6027
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang, Dapeng Hu, Jiashi Feng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6028-6039
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Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Peter Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6040-6049
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Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard Fair; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6050-6060
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6061-6071
Hierarchical Verification for Adversarial Robustness
Cong Han Lim, Raquel Urtasun, Ersin Yumer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6072-6082
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin, Chi Jin, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6083-6093
Extrapolation for Large-batch Training in Deep Learning
Tao Lin, Lingjing Kong, Sebastian Stich, Martin Jaggi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6094-6104
On the Theoretical Properties of the Network Jackknife
Qiaohui Lin, Robert Lunde, Purnamrita Sarkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6105-6115
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6116-6126
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Zinan Lin, Kiran Thekumparampil, Giulia Fanti, Sewoong Oh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6127-6139
Improving Generative Imagination in Object-Centric World Models
Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6140-6149
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6150-6160
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6161-6171
Time-aware Large Kernel Convolutions
Vasileios Lioutas, Yuhong Guo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6172-6183
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Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Yao Liu, Pierre-Luc Bacon, Emma Brunskill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6184-6193
Sparse Shrunk Additive Models
Guodong Liu, Hong Chen, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6194-6204
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6205-6215
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6216-6225
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Yang Liu, Hongyi Guo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6226-6236
An Imitation Learning Approach for Cache Replacement
Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6237-6247
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6248-6258
Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6259-6270
A Chance-Constrained Generative Framework for Sequence Optimization
Xianggen Liu, Qiang Liu, Sen Song, Jian Peng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6271-6281
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O’Reilly; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6282-6293
Median Matrix Completion: from Embarrassment to Optimality
Weidong Liu, Xiaojun Mao, Raymond K. W. Wong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6294-6304
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6305-6315
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6316-6326
Learning to Encode Position for Transformer with Continuous Dynamical Model
Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6327-6335
Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu, Friedemann Zenke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6336-6347
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6348-6359
Too Relaxed to Be Fair
Michael Lohaus, Michael Perrot, Ulrike Von Luxburg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6360-6369
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6370-6381
Error Estimation for Sketched SVD via the Bootstrap
Miles Lopes, N. Benjamin Erichson, Michael Mahoney; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6382-6392
Differentiating through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6393-6403
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6404-6414
Moniqua: Modulo Quantized Communication in Decentralized SGD
Yucheng Lu, Christopher De Sa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6415-6425
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6426-6436
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6437-6447
Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6448-6458
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
Siqiang Luo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6459-6467
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Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6468-6478
Adversarial Nonnegative Matrix Factorization
Lei Luo, Yanfu Zhang, Heng Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6479-6488
Learning Algebraic Multigrid Using Graph Neural Networks
Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6489-6499
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Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6500-6510
Bandits with Adversarial Scaling
Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6511-6521
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Efficient Continuous Pareto Exploration in Multi-Task Learning
Pingchuan Ma, Tao Du, Wojciech Matusik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6522-6531
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6532-6542
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, James Bailey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6543-6553
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
Runchao Ma, Qihang Lin, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6554-6564
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Shaocong Ma, Yi Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6565-6574
Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6575-6585
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
Debabrata Mahapatra, Vaibhav Rajan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6597-6607
How recurrent networks implement contextual processing in sentiment analysis
Niru Maheswaranathan, David Sussillo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6608-6619
Anderson Acceleration of Proximal Gradient Methods
Vien Mai, Mikael Johansson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6620-6629
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization
Vien Mai, Mikael Johansson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6630-6639
Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini, Eric Wong, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6640-6650
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6651-6660
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik Johansson, John Guttag, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6661-6671
Optimal transport mapping via input convex neural networks
Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6672-6681
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6682-6691
From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtarik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6692-6701
Adaptive Gradient Descent without Descent
Yura Malitsky, Konstantin Mishchenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6702-6712
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, Sueyeon Chung; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6713-6723
Adaptive Adversarial Multi-task Representation Learning
Yuren Mao, Weiwei Liu, Xuemin Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6724-6733
On Learning Sets of Symmetric Elements
Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6734-6744
Stochastically Dominant Distributional Reinforcement Learning
John Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6745-6754
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez, Martin Bertran, Guillermo Sapiro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6755-6764
Predictive Multiplicity in Classification
Charles Marx, Flavio Calmon, Berk Ustun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6765-6774
Adding seemingly uninformative labels helps in low data regimes
Christos Matsoukas, Albert Bou Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6775-6784
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Robert Mattila, Cristian Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6785-6796
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On Approximate Thompson Sampling with Langevin Algorithms
Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6797-6807
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6808-6819
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6820-6829
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda, Jean De Becdelievre, Jayesh Gupta, Ilan Kroo, Mykel Kochenderfer, Zachary Manchester; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6830-6840
Randomized Block-Diagonal Preconditioning for Parallel Learning
Celestine Mendler-Dünner, Aurelien Lucchi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6841-6851
Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6852-6861
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6862-6873
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
Francesca Mignacco, Florent Krzakala, Yue Lu, Pierfrancesco Urbani, Lenka Zdeborova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6874-6883
Projective Preferential Bayesian Optimization
Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6884-6892
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
Zoltán Milacski, Barnabas Poczos, Andras Lorincz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6893-6904
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The Effect of Natural Distribution Shift on Question Answering Models
John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6905-6916
Strategic Classification is Causal Modeling in Disguise
John Miller, Smitha Milli, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6917-6926
Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6927-6937
Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6938-6949
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Coresets for Data-efficient Training of Machine Learning Models
Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6950-6960
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6961-6971
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6972-6986
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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6987-6998
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
Zahra Monfared, Daniel Durstewitz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6999-7009
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7010-7021
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An end-to-end approach for the verification problem: learning the right distance
Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm, Tiago Falk; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7022-7033
Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7034-7044
Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7045-7054
Explainable k-Means and k-Medians Clustering
Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7055-7065
Fair Learning with Private Demographic Data
Hussein Mozannar, Mesrob Ohannessian, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7066-7075
Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar, David Sontag; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7076-7087
Continuous-time Lower Bounds for Gradient-based Algorithms
Michael Muehlebach, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7088-7096
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7097-7107
Unique Properties of Flat Minima in Deep Networks
Rotem Mulayoff, Tomer Michaeli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7108-7118
Fast computation of Nash Equilibria in Imperfect Information Games
Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7119-7129
Missing Data Imputation using Optimal Transport
Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7130-7140
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7141-7152
Full Law Identification in Graphical Models of Missing Data: Completeness Results
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7153-7163
Voice Separation with an Unknown Number of Multiple Speakers
Eliya Nachmani, Yossi Adi, Lior Wolf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7164-7175
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Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7176-7185
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From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
Sai Ganesh Nagarajan, David Balduzzi, Georgios Piliouras; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7186-7196
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, Tijmen Blankevoort; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7197-7206
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair, Silvio Savarese, Chelsea Finn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7207-7219
PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter Battaglia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7220-7229
Oracle Efficient Private Non-Convex Optimization
Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7243-7252
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7253-7262
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors
Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7263-7272
Aggregation of Multiple Knockoffs
Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7283-7293
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7294-7305
Graph Homomorphism Convolution
Hoang Nguyen, Takanori Maehara; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7306-7316
Robust Bayesian Classification Using An Optimistic Score Ratio
Viet Anh Nguyen, Nian Si, Jose Blanchet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7327-7337
Streaming k-Submodular Maximization under Noise subject to Size Constraint
Lan Nguyen, My T. Thai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7338-7347
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Vlad Niculae, Andre Martins; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7348-7359
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7360-7369
Supervised learning: no loss no cry
Richard Nock, Aditya Menon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7370-7380
Consistent Structured Prediction with Max-Min Margin Markov Networks
Alex Nowak, Francis Bach, Alessandro Rudi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7381-7391
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T-Basis: a Compact Representation for Neural Networks
Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7392-7404
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Eliminating the Invariance on the Loss Landscape of Linear Autoencoders
Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan Shell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7405-7413
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka, Tatsuya Matsuoka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7414-7423
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, Daniel Nikovski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7424-7433
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Edouard Oyallon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7434-7444
Learning to Score Behaviors for Guided Policy Optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7445-7454
Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut, Jinhyung Lee, Liam Paninski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7455-7465
Recovery of Sparse Signals from a Mixture of Linear Samples
Soumyabrata Pal, Arya Mazumdar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7466-7475
Adversarial Mutual Information for Text Generation
Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7476-7486
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew Botvinick, Nicolas Heess, Raia Hadsell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7487-7498
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park, Kenneth Carr, Stephan Zheng, Yisong Yue, Rose Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7499-7509
Meta Variance Transfer: Learning to Augment from the Others
Seong-Jin Park, Seungju Han, Ji-Won Baek, Insoo Kim, Juhwan Song, Hae Beom Lee, Jae-Joon Han, Sung Ju Hwang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7510-7520
Structured Policy Iteration for Linear Quadratic Regulator
Youngsuk Park, Ryan Rossi, Zheng Wen, Gang Wu, Handong Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7521-7531
Regularized Optimal Transport is Ground Cost Adversarial
François-Pierre Paty, Marco Cuturi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7532-7542
Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse, Ishan Durugkar, Josiah Hanna, Peter Stone; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7543-7552
Acceleration through spectral density estimation
Fabian Pedregosa, Damien Scieur; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7553-7562
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7563-7574
Learning Selection Strategies in Buchberger’s Algorithm
Dylan Peifer, Michael Stillman, Daniel Halpern-Leistner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7575-7585
Non-Autoregressive Neural Text-to-Speech
Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7586-7598
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Performative Prediction
Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7599-7609
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7610-7619
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Budgeted Online Influence Maximization
Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7620-7631
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks
Adeel Pervez, Taco Cohen, Efstratios Gavves; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7632-7640
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7641-7651
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7652-7662
IPBoost – Non-Convex Boosting via Integer Programming
Marc Pfetsch, Sebastian Pokutta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7663-7672
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7673-7682
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
Hai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, Dejing Dou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7683-7694
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Mert Pilanci, Tolga Ergen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7695-7705
WaveFlow: A Compact Flow-based Model for Raw Audio
Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7706-7716
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Randomization matters How to defend against strong adversarial attacks
Rafael Pinot, Raphael Ettedgui, Geovani Rizk, Yann Chevaleyre, Jamal Atif; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7717-7727
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7728-7738
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Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Konstantinos Pitas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7739-7749
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, Jimmy Ba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7750-7761
Explaining Groups of Points in Low-Dimensional Representations
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7762-7771
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Sebastian Pokutta, Mohit Singh, Alfredo Torrico; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7772-7782
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7783-7792
SoftSort: A Continuous Relaxation for the argsort Operator
Sebastian Prillo, Julian Eisenschlos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7793-7802
Graph-based Nearest Neighbor Search: From Practice to Theory
Liudmila Prokhorenkova, Aleksandr Shekhovtsov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7803-7813
Adversarial Risk via Optimal Transport and Optimal Couplings
Muni Sreenivas Pydi, Varun Jog; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7814-7823
Deep Isometric Learning for Visual Recognition
Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7824-7835
Unsupervised Speech Decomposition via Triple Information Bottleneck
Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David Cox; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7836-7846
Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7847-7856
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
Shuang Qiu, Xiaohan Wei, Zhuoran Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7857-7866
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7867-7876
DeepCoDA: personalized interpretability for compositional health data
Thomas Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7877-7886
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
Akbar Rafiey, Yuichi Yoshida; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7887-7897
Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7898-7908
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7909-7919
Fast Adaptation to New Environments via Policy-Dynamics Value Functions
Roberta Raileanu, Max Goldstein, Arthur Szlam, Rob Fergus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7920-7931
Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj, Yoram Bresler, Bo Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7932-7942
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Aditya Rajagopal, Diederik Vink, Stylianos Venieris, Christos-Savvas Bouganis; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7943-7952
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7953-7963
Closing the convergence gap of SGD without replacement
Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7964-7973
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, Adish Singla; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7974-7984
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7985-7995
Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:7996-8006
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AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Esteban Real, Chen Liang, David So, Quoc Le; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8007-8019
Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy, Anca Dragan, Sergey Levine, Shane Legg, Jan Leike; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8020-8029
Optimistic Bounds for Multi-output Learning
Henry Reeve, Ata Kaban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8030-8040
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8041-8050
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
Wenbo Ren, Jia Liu, Ness Shroff; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8051-8072
NetGAN without GAN: From Random Walks to Low-Rank Approximations
Luca Rendsburg, Holger Heidrich, Ulrike Von Luxburg; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8073-8082
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sebastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, Kyle Cranmer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8083-8092
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8093-8104
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8105-8115
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Laura Rieger, Chandan Singh, William Murdoch, Bin Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8116-8126
Strength from Weakness: Fast Learning Using Weak Supervision
Joshua Robinson, Stefanie Jegelka, Suvrit Sra; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8127-8136
On Semi-parametric Inference for BART
Veronika Rockova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8137-8146
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8147-8157
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Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Bjorkegren, Moritz Hardt, Joshua Blumenstock; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8158-8168
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8169-8177
Attentive Group Equivariant Convolutional Networks
David Romero, Erik Bekkers, Jakub Tomczak, Mark Hoogendoorn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8188-8199
Finite-Time Convergence in Continuous-Time Optimization
Orlando Romero, Mouhacine Benosman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8200-8209
Near-optimal Regret Bounds for Stochastic Shortest Path
Aviv Rosenberg, Alon Cohen, Yishay Mansour, Haim Kaplan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8210-8219
Predicting Choice with Set-Dependent Aggregation
Nir Rosenfeld, Kojin Oshiba, Yaron Singer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8220-8229
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, Zico Kolter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8230-8241
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Bjorn Ommer, Joseph Paul Cohen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8242-8252
FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8253-8265
Simple and sharp analysis of k-means||
Václav Rozhoň; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8266-8275
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Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, Stephen Roberts; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8276-8285
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Inter-domain Deep Gaussian Processes
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8286-8294
Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya Ryali, John Hopfield, Leopold Grinberg, Dmitry Krotov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8295-8306
Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah, Ali Shafahi, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8307-8315
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Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato, Elad Yom-Tov; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8316-8325
Radioactive data: tracing through training
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Herve Jegou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8326-8335
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil Saeed, Snigdha Panigrahi, Caroline Uhler; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8336-8345
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8346-8356
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
Aadirupa Saha, Pierre Gaillard, Michal Valko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8357-8366
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Aadirupa Saha, Aditya Gopalan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8367-8376
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, Michael Tschantz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8377-8387
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8388-8397
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito, Shota Yasui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8398-8407
Inferring DQN structure for high-dimensional continuous control
Andrey Sakryukin, Chedy Raissi, Mohan Kankanhalli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8408-8416
The Performance Analysis of Generalized Margin Maximizers on Separable Data
Fariborz Salehi, Ehsan Abbasi, Babak Hassibi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8417-8426
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Sudeep Salgia, Qing Zhao, Sattar Vakili; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8427-8437
A Quantile-based Approach for Hyperparameter Transfer Learning
David Salinas, Huibin Shen, Valerio Perrone; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8438-8448
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert Kohn, Mattias Villani, Minh-Ngoc Tran; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8449-8458
Learning to Simulate Complex Physics with Graph Networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter Battaglia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8459-8468
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8469-8479
Explicit Gradient Learning for Black-Box Optimization
Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8480-8490
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Detecting Out-of-Distribution Examples with Gram Matrices
Chandramouli Shama Sastry, Sageev Oore; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8491-8501
Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija, Philip Amortila, Joelle Pineau; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8502-8511
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8512-8521
Harmonic Decompositions of Convolutional Networks
Meyer Scetbon, Zaid Harchaoui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8522-8532
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Implicit competitive regularization in GANs
Florian Schaefer, Hongkai Zheng, Animashree Anandkumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8533-8544
Off-Policy Actor-Critic with Shared Experience Replay
Simon Schmitt, Matteo Hessel, Karen Simonyan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8545-8554
Discriminative Adversarial Search for Abstractive Summarization
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8555-8564
Universal Average-Case Optimality of Polyak Momentum
Damien Scieur, Fabian Pedregosa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8565-8572
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Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8573-8582
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8583-8592
An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8593-8603
Optimistic Policy Optimization with Bandit Feedback
Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8604-8613
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Neural Kernels Without Tangents
Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8614-8623
Learning Robot Skills with Temporal Variational Inference
Tanmay Shankar, Abhinav Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8624-8633
Evaluating Machine Accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8634-8644
Channel Equilibrium Networks for Learning Deep Representation
Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8645-8654
ControlVAE: Controllable Variational Autoencoder
Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8655-8664
Causal Strategic Linear Regression
Yonadav Shavit, Benjamin Edelman, Brian Axelrod; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8676-8686
Adaptive Sampling for Estimating Probability Distributions
Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8687-8696
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8697-8706
Deep Reinforcement Learning with Robust and Smooth Policy
Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8707-8718
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Tianxiao Shen, Jonas Mueller, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8719-8729
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela Van Der Schaar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8730-8740
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen, Zhewei Yao, Amir Gholami, Michael Mahoney, Kurt Keutzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8741-8751
Extreme Multi-label Classification from Aggregated Labels
Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8752-8762
One-shot Distributed Ridge Regression in High Dimensions
Yue Sheng, Edgar Dobriban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8763-8772
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Alexander Shevchenko, Marco Mondelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8773-8784
Incremental Sampling Without Replacement for Sequence Models
Kensen Shi, David Bieber, Charles Sutton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8785-8795
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Yunpeng Shi, Gilad Lerman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8796-8806
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Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8807-8817
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8818-8827
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Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8828-8839
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Wenxian Shi, Hao Zhou, Ning Miao, Lei Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8840-8851
On Conditional Versus Marginal Bias in Multi-Armed Bandits
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8852-8861
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung Bui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8862-8871
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8872-8883
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Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
Nian Si, Fan Zhang, Zhengyuan Zhou, Jose Blanchet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8884-8894
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8895-8904
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique, Paul Weng, Matthieu Zimmer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8905-8915
Deep Gaussian Markov Random Fields
Per Sidén, Fredrik Lindsten; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8916-8926
Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8927-8936
Naive Exploration is Optimal for Online LQR
Max Simchowitz, Dylan Foster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8937-8948
A Generative Model for Molecular Distance Geometry
Gregor Simm, Jose Miguel Hernandez-Lobato; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8949-8958
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8959-8969
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gurbuzbalaban; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8970-8980
Second-Order Provable Defenses against Adversarial Attacks
Sahil Singla, Soheil Feizi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8981-8991
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha, Matthew O’Kelly, Hongrui Zheng, Rahul Mangharam, John Duchi, Russ Tedrake; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8992-9004
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Small-GAN: Speeding up GAN Training using Core-Sets
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9005-9015
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Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
John Sipple; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9016-9025
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Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Vidyashankar Sivakumar, Steven Wu, Arindam Banerjee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9026-9035
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Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9036-9045
When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt, Maximilian Granz, Tim Landgraf; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9046-9057
On the Generalization Benefit of Noise in Stochastic Gradient Descent
Samuel Smith, Erich Elsen, Soham De; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9058-9067
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis, Petros Maragos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9068-9077
Bridging the Gap Between f-GANs and Wasserstein GANs
Jiaming Song, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9078-9087
Provably Efficient Model-based Policy Adaptation
Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9088-9098
Hypernetwork approach to generating point clouds
Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9099-9108
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Robustness to Spurious Correlations via Human Annotations
Megha Srivastava, Tatsunori Hashimoto, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9109-9119
Which Tasks Should Be Learned Together in Multi-task Learning?
Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9120-9132
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Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Adam Stooke, Joshua Achiam, Pieter Abbeel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9133-9143
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos, Sam Wiseman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9144-9154
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz, Matthias Hein, Bernt Schiele; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9155-9166
Doubly robust off-policy evaluation with shrinkage
Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9167-9176
Task Understanding from Confusing Multi-task Data
Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9177-9186
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Dijia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9187-9195
Adaptive Estimator Selection for Off-Policy Evaluation
Yi Su, Pavithra Srinath, Akshay Krishnamurthy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9196-9205
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, Jeffrey Clune; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9206-9216
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Haoran Sun, Songtao Lu, Mingyi Hong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9217-9228
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, Moritz Hardt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9229-9248
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An EM Approach to Non-autoregressive Conditional Sequence Generation
Zhiqing Sun, Yiming Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9249-9258
The Shapley Taylor Interaction Index
Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9259-9268
The Many Shapley Values for Model Explanation
Mukund Sundararajan, Amir Najmi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9269-9278
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Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9279-9288
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Joshua Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9289-9299
Multi-Agent Routing Value Iteration Network
Quinlan Sykora, Mengye Ren, Raquel Urtasun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9300-9310
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9311-9323
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Quantized Decentralized Stochastic Learning over Directed Graphs
Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9324-9333
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9334-9345
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam, David Dunson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9346-9355
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DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan, Mehul Motani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9356-9366
Reinforcement Learning for Integer Programming: Learning to Cut
Yunhao Tang, Shipra Agrawal, Yuri Faenza; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9367-9376
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
Wenpin Tang, Xin Guo, Fengmin Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9377-9386
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9387-9396
Taylor Expansion Policy Optimization
Yunhao Tang, Michal Valko, Remi Munos; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9397-9406
Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9407-9417
Learning disconnected manifolds: a no GAN’s land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9418-9427
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9428-9437
Sparse Sinkhorn Attention
Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9438-9447
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Inductive Relation Prediction by Subgraph Reasoning
Komal Teru, Etienne Denis, Will Hamilton; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9448-9457
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9458-9469
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension
Yuandong Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9470-9480
Sequential Transfer in Reinforcement Learning with a Generative Model
Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9481-9492
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
Bahareh Tolooshams, Andrew Song, Simona Temereanca, Demba Ba; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9493-9503
Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9504-9513
Choice Set Optimization Under Discrete Choice Models of Group Decisions
Kiran Tomlinson, Austin Benson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9514-9525
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong, Jessie Huang, Guy Wolf, David Van Dijk, Smita Krishnaswamy; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9526-9536
Alleviating Privacy Attacks via Causal Learning
Shruti Tople, Amit Sharma, Aditya Nori; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9537-9547
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth, Harald Oberhauser; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9548-9560
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Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9561-9571
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
Quoc Tran-Dinh, Nhan Pham, Lam Nguyen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9572-9582
Bayesian Differential Privacy for Machine Learning
Aleksei Triastcyn, Boi Faltings; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9583-9592
Single Point Transductive Prediction
Nilesh Tripuraneni, Lester Mackey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9593-9602
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi, Jiachen Yang, Hongyuan Zha; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9603-9613
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9614-9624
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9625-9635
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9636-9647
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network
Javier Turek, Shailee Jain, Vy Vo, Mihai Capotă, Alexander Huth, Theodore Willke; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9648-9658
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara, Jiawei Huang, Nan Jiang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9659-9668
StochasticRank: Global Optimization of Scale-Free Discrete Functions
Aleksei Ustimenko, Liudmila Prokhorenkova; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9669-9679
Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William Macready; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9680-9689
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9690-9700
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9701-9711
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Linear bandits with Stochastic Delayed Feedback
Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9712-9721
Non-Stationary Delayed Bandits with Intermediate Observations
Claire Vernade, Andras Gyorgy, Timothy Mann; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9722-9732
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z Leibo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9733-9742
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Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9754-9764
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9765-9774
Conditional gradient methods for stochastically constrained convex minimization
Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9775-9785
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Andrey Voynov, Artem Babenko; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9786-9796
Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi, Yanan Sui; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9797-9806
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
Chengcheng Wan, Henry Hoffmann, Shan Lu, Michael Maire; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9807-9817
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Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity
Yuanyu Wan, Wei-Wei Tu, Lijun Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9818-9828
On the Global Optimality of Model-Agnostic Meta-Learning
Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9837-9846
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching
Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9847-9856
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Self-Modulating Nonparametric Event-Tensor Factorization
Zheng Wang, Xinqi Chu, Shandian Zhe; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9857-9867
Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Guanyi Wang, Santanu Dey; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9868-9875
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9876-9886
Non-separable Non-stationary random fields
Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark Steel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9887-9897
Continuously Indexed Domain Adaptation
Hao Wang, Hao He, Dina Katabi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9898-9907
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9908-9918
Frustratingly Simple Few-Shot Object Detection
Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9919-9928
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang, Phillip Isola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9929-9939
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, Kenneth Stanley; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9940-9951
Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, Yanan Fan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9952-9962
Deep Streaming Label Learning
Zhen Wang, Liu Liu, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9963-9972
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao, Donald Lee; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9973-9982
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9983-9995
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When deep denoising meets iterative phase retrieval
Yaotian Wang, Xiaohang Sun, Jason Fleischer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10007-10017
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Qi Wang, Herke Van Hoof; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10018-10028
Loss Function Search for Face Recognition
Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10029-10038
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Sequential Cooperative Bayesian Inference
Junqi Wang, Pei Wang, Patrick Shafto; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10039-10049
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Yuh-Shyang Wang, Lily Weng, Luca Daniel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10050-10059
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable
Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10060-10069
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10070-10080
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10081-10091
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Lingxiao Wang, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10092-10103
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10104-10114
Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10115-10125
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Peng Wang, Zirui Zhou, Anthony Man-Cho So; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10126-10135
Learning Representations that Support Extrapolation
Taylor Webb, Zachary Dulberg, Steven Frankland, Alexander Petrov, Randall O’Reilly, Jonathan Cohen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10136-10146
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10158-10169
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei, Mehdi Jafarnia Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10170-10180
The Implicit and Explicit Regularization Effects of Dropout
Colin Wei, Sham Kakade, Tengyu Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10181-10192
Online Control of the False Coverage Rate and False Sign Rate
Asaf Weinstein, Aaditya Ramdas; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10193-10202
Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10203-10213
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen, Russell Greiner, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10214-10224
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Yuxin Wen, Shuai Li, Kui Jia; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10225-10235
Amortised Learning by Wake-Sleep
Li Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10236-10247
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10248-10259
Predictive Sampling with Forecasting Autoregressive Models
Auke Wiggers, Emiel Hoogeboom; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10260-10269
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
William Wilkinson, Paul Chang, Michael Andersen, Arno Solin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10270-10281
Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian Williamson, Jean Feng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10282-10291
Efficiently sampling functions from Gaussian process posteriors
James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10292-10302
Learning to Rank Learning Curves
Martin Wistuba, Tejaswini Pedapati; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10303-10312
Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10313-10323
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
David Woodruff, Amir Zandieh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10324-10333
Is Local SGD Better than Minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10334-10343
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu, Vladimir Braverman, Lin Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10344-10354
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan Davidson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10355-10366
On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10367-10376
Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu, Allen Wang, Yaoliang Yu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10377-10387
Sequence Generation with Mixed Representations
Lijun Wu, Shufang Xie, Yingce Xia, Yang Fan, Jian-Huang Lai, Tao Qin, Tieyan Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10388-10398
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Adversarial Robustness via Runtime Masking and Cleansing
Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10399-10409
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu, Hongyang Zhang, Gregory Valiant, Christopher Re; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10410-10420
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem Van De Meent; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10421-10431
Continuous Graph Neural Networks
Louis-Pascal Xhonneux, Meng Qu, Jian Tang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10432-10441
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Yunhua Xiang, Noah Simon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10442-10451
Generative Flows with Matrix Exponential
Changyi Xiao, Ligang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10452-10461
Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10462-10472
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Yuxuan Xie, Jilles Dibangoye, Olivier Buffet; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10473-10482
Maximum-and-Concatenation Networks
Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10483-10494
Zeno++: Robust Fully Asynchronous SGD
Cong Xie, Sanmi Koyejo, Indranil Gupta; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10495-10503
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems
Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10504-10513
On the Number of Linear Regions of Convolutional Neural Networks
Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, Ling Shao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10514-10523
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tieyan Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10524-10533
On Variational Learning of Controllable Representations for Text without Supervision
Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10534-10543
Class-Weighted Classification: Trade-offs and Robust Approaches
Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10544-10554
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A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu, Quanquan Gu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10555-10565
Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory
Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10566-10575
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Learning Autoencoders with Relational Regularization
Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10576-10586
Learning Factorized Weight Matrix for Joint Filtering
Xiangyu Xu, Yongrui Ma, Wenxiu Sun; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10587-10596
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Variational Label Enhancement
Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10597-10606
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Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10607-10616
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MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam Kosiorek, Yee Whye Teh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10617-10627
Video Prediction via Example Guidance
Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10628-10637
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan Adams; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10638-10647
Feature Selection using Stochastic Gates
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10648-10659
Stochastic Optimization for Non-convex Inf-Projection Problems
Yan Yan, Yi Xu, Lijun Zhang, Wang Xiaoyu, Tianbao Yang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10660-10669
Variational Bayesian Quantization
Yibo Yang, Robert Bamler, Stephan Mandt; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10670-10680
Energy-Based Processes for Exchangeable Data
Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10681-10692
Randomized Smoothing of All Shapes and Sizes
Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10693-10705
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, Zhongyu Wei; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10706-10715
Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson, Dr.Regina Barzilay, Tommi Jaakkola; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10716-10726
On the consistency of top-k surrogate losses
Forest Yang, Sanmi Koyejo; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10727-10735
Interpolation between Residual and Non-Residual Networks
Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10736-10745
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin Yang, Mengdi Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10746-10756
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Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10757-10766
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10767-10777
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10778-10788
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Searching to Exploit Memorization Effect in Learning with Noisy Labels
Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10789-10798
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Michihiro Yasunaga, Percy Liang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10799-10808
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Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Hui Ye, Zhiyu Chen, Da-Han Wang, Brian Davison; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10809-10819
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Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10820-10830
It’s Not What Machines Can Learn, It’s What We Cannot Teach
Gal Yehuda, Moshe Gabel, Assaf Schuster; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10831-10841
Data Valuation using Reinforcement Learning
Jinsung Yoon, Sercan Arik, Tomas Pfister; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10842-10851
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10852-10860
Robustifying Sequential Neural Processes
Jaesik Yoon, Gautam Singh, Sungjin Ahn; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10861-10870
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10871-10880
Graph Structure of Neural Networks
Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10881-10891
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu, Shih-Kang Chao, Guang Cheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10892-10901
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach
Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10902-10912
Label-Noise Robust Domain Adaptation
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10913-10924
Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu, Yueming Lyu, Ivor Tsang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10925-10935
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Wenhui Yu, Zheng Qin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10936-10945
Federated Learning with Only Positive Labels
Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10946-10956
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Training Deep Energy-Based Models with f-Divergence Minimization
Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10957-10967
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10968-10977
Learning Near Optimal Policies with Low Inherent Bellman Error
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10978-10989
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van Den Broeck; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10990-11000
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew Hausknecht; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11001-11011
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Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Junzhe Zhang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11012-11022
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Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang, Aws Albarghouthi, Loris D’Antoni; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11023-11032
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Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Youzhi Zhang, Bo An; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11033-11043
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11044-11054
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11055-11065
Learning the Valuations of a $k$-demand Agent
Hanrui Zhang, Vincent Conitzer; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11066-11075
A Tree-Structured Decoder for Image-to-Markup Generation
Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Lirong Dai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11076-11085
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Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Han Zhang, Xi Gao, Jacob Unterman, Tom Arodz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11086-11095
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Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Richard Zhang, Daniel Golovin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11096-11105
Spread Divergence
Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11106-11116
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11117-11128
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Steven Wu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11129-11140
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11141-11152
Optimal Estimator for Unlabeled Linear Regression
Hang Zhang, Ping Li; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11153-11162
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11163-11172
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11173-11182
Self-Attentive Hawkes Process
Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11183-11193
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Shangtong Zhang, Bo Liu, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11194-11203
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11204-11213
Invariant Causal Prediction for Block MDPs
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11214-11224
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11225-11234
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Wei Zhang, Thomas Panum, Somesh Jha, Prasad Chalasani, David Page; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11235-11245
Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11246-11255
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Brian Zhang, Tuomas Sandholm; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11256-11267
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11268-11277
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11278-11287
A Flexible Latent Space Model for Multilayer Networks
Xuefei Zhang, Songkai Xue, Ji Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11288-11297
Perceptual Generative Autoencoders
Zijun Zhang, Ruixiang Zhang, Zongpeng Li, Yoshua Bengio, Liam Paull; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11298-11306
Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang, Yang Zhao, Changyou Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11307-11316
Learning with Feature and Distribution Evolvable Streams
Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11317-11327
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter Liu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11328-11339
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao, Yulai Cong, Lawrence Carin; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11340-11351
On Learning Language-Invariant Representations for Universal Machine Translation
Han Zhao, Junjie Hu, Andrej Risteski; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11352-11364
Do RNN and LSTM have Long Memory?
Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11365-11375
Feature Quantization Improves GAN Training
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11376-11386
Individual Calibration with Randomized Forecasting
Shengjia Zhao, Tengyu Ma, Stefano Ermon; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11387-11397
Smaller, more accurate regression forests using tree alternating optimization
Arman Zharmagambetov, Miguel Carreira-Perpinan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11398-11408
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11409-11419
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie Su; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11420-11435
What Can Learned Intrinsic Rewards Capture?
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado Van Hasselt, David Silver, Satinder Singh; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11436-11446
Error-Bounded Correction of Noisy Labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11447-11457
Robust Graph Representation Learning via Neural Sparsification
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11458-11468
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer
Anton Zhiyanov, Alexey Drutsa; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11469-11480
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Zixin Zhong, Wang Chi Cheung, Vincent Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11481-11491
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou, Lihong Li, Quanquan Gu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11492-11502
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11503-11512
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Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11513-11522
Time-Consistent Self-Supervision for Semi-Supervised Learning
Tianyi Zhou, Shengjie Wang, Jeff Bilmes; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11523-11533
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11534-11545
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11546-11555
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization
Pan Zhou, Xiao-Tong Yuan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11556-11565
Robust Outlier Arm Identification
Yinglun Zhu, Sumeet Katariya, Robert Nowak; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11566-11575
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael Zhu, Chang Liu, Jun Zhu; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11576-11587
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Liangyu Zhu, Wenbin Lu, Rui Song; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11588-11598
Thompson Sampling Algorithms for Mean-Variance Bandits
Qiuyu Zhu, Vincent Tan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11599-11608
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu, Xiao Zhang, David Evans; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11609-11618
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu, Lei Zhao; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11619-11628
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu, Zeyu Zheng; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11629-11638
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11639-11649
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Learning Optimal Tree Models under Beam Search
Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11650-11659
Laplacian Regularized Few-Shot Learning
Imtiaz Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11660-11670
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Influenza Forecasting Framework based on Gaussian Processes
Christoph Zimmer, Reza Yaesoubi; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11671-11679
A general recurrent state space framework for modeling neural dynamics during decision-making
David Zoltowski, Jonathan Pillow, Scott Linderman; Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11680-11691
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