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Editors: Kamalika Chaudhuri, Ruslan Salakhutdinov
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AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1-10
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:11-20
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:21-29
Communication-Constrained Inference and the Role of Shared Randomness
Jayadev Acharya, Clement Canonne, Himanshu Tyagi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:30-39
Distributed Learning with Sublinear Communication
Jayadev Acharya, Chris De Sa, Dylan Foster, Karthik Sridharan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:40-50
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
Jayadev Acharya, Ziteng Sun; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:51-60
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Learning Models from Data with Measurement Error: Tackling Underreporting
Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:61-70
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
Tameem Adel, Adrian Weller; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:71-81
PAC Learnability of Node Functions in Networked Dynamical Systems
Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:82-91
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Static Automatic Batching In TensorFlow
Ashish Agarwal; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:92-101
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Efficient Full-Matrix Adaptive Regularization
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:102-110
Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:111-119
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:120-129
Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:130-140
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:141-150
Understanding the Impact of Entropy on Policy Optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:151-160
Fairwashing: the risk of rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:161-170
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:171-180
Projections for Approximate Policy Iteration Algorithms
Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:181-190
Validating Causal Inference Models via Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:191-201
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Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:202-211
Graph Element Networks: adaptive, structured computation and memory
Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelbling; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:212-222
Analogies Explained: Towards Understanding Word Embeddings
Carl Allen, Timothy Hospedales; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:223-231
Infinite Mixture Prototypes for Few-shot Learning
Kelsey Allen, Evan Shelhamer, Hanul Shin, Joshua Tenenbaum; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:232-241
A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:242-252
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Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:253-262
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Kareem Amin, Alex Kulesza, Andres Munoz, Sergei Vassilvtiskii; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:263-271
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
Marco Ancona, Cengiz Oztireli, Markus Gross; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:272-281
Scaling Up Ordinal Embedding: A Landmark Approach
Jesse Anderton, Javed Aslam; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:282-290
Sorting Out Lipschitz Function Approximation
Cem Anil, James Lucas, Roger Grosse; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:291-301
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:302-311
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, Kevin Mcguinness; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:312-321
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, Ruosong Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:322-332
Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:333-343
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Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:344-353
Bayesian Optimization of Composite Functions
Raul Astudillo, Peter Frazier; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:354-363
Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations
Kubilay Atasu, Thomas Mittelholzer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:364-373
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavković; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:374-384
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
Sergul Aydore, Bertrand Thirion, Gael Varoquaux; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:385-394
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior
Fadhel Ayed, Juho Lee, Francois Caron; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:395-404
Scalable Fair Clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:405-413
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:414-423
Provable Guarantees for Gradient-Based Meta-Learning
Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:424-433
Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:434-443
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Muhammed Fatih Balın, Abubakar Abid, James Zou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:444-453
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:454-463
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Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly Stachenfeld, Pushmeet Kohli, Peter Battaglia, Jessica Hamrick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:464-474
Learning to Route in Similarity Graphs
Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:475-484
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A Personalized Affective Memory Model for Improving Emotion Recognition
Pablo Barros, German Parisi, Stefan Wermter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:485-494
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:495-504
Pareto Optimal Streaming Unsupervised Classification
Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:505-514
Categorical Feature Compression via Submodular Optimization
Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:515-523
Noise2Self: Blind Denoising by Self-Supervision
Joshua Batson, Loic Royer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:524-533
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Efficient optimization of loops and limits with randomized telescoping sums
Alex Beatson, Ryan P Adams; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:534-543
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:544-552
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck, Jan Peters, Patrick Van Der Smagt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:553-562
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings
Sima Behpour, Anqi Liu, Brian Ziebart; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:563-572
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Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:573-582
Greedy Layerwise Learning Can Scale To ImageNet
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:583-593
Overcoming Multi-model Forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:594-603
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:604-613
Adversarially Learned Representations for Information Obfuscation and Inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapiro; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:614-623
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:624-633
Analyzing Federated Learning through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:634-643
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
Yatao Bian, Joachim Buhmann, Andreas Krause; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:644-653
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, Mark Van Der Laan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:654-663
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A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:664-674
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau, Tomer Michaeli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:675-685
Correlated bandits or: How to minimize mean-squared error online
Vinay Praneeth Boda, Prashanth L.A.; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:686-694
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Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:695-704
Online Variance Reduction with Mixtures
Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:705-714
Compositional Fairness Constraints for Graph Embeddings
Avishek Bose, William Hamilton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:715-724
Unreproducible Research is Reproducible
Xavier Bouthillier, César Laurent, Pascal Vincent; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:725-734
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Blended Conditonal Gradients
Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:735-743
Coresets for Ordered Weighted Clustering
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:744-753
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Target Tracking for Contextual Bandits: Application to Demand Side Management
Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:754-763
Active Manifolds: A non-linear analogue to Active Subspaces
Robert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoff; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:764-772
Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, Jennifer Listgarten; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:773-782
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:783-792
Deep Counterfactual Regret Minimization
Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:793-802
Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:803-811
Low Latency Privacy Preserving Inference
Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:812-821
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
Alon Brutzkus, Amir Globerson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:822-830
Adversarial examples from computational constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price, Ilya Razenshteyn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:831-840
Self-similar Epochs: Value in arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:841-850
Learning Generative Models across Incomparable Spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:851-861
Rates of Convergence for Sparse Variational Gaussian Process Regression
David Burt, Carl Edward Rasmussen, Mark Van Der Wilk; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:862-871
What is the Effect of Importance Weighting in Deep Learning?
Jonathon Byrd, Zachary Lipton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:872-881
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
Yongqiang Cai, Qianxiao Li, Zuowei Shen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:882-890
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Bugra Can, Mert Gurbuzbalaban, Lingjiong Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:891-901
Active Embedding Search via Noisy Paired Comparisons
Gregory Canal, Andy Massimino, Mark Davenport, Christopher Rozell; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:902-911
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Junyu Cao, Wei Sun; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:912-920
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games
Adrian Rivera Cardoso, Jacob Abernethy, He Wang, Huan Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:921-930
Automated Model Selection with Bayesian Quadrature
Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:931-940
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Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip Thomas; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:941-950
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Chun-Hao Chang, Mingjie Mai, Anna Goldenberg; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:951-960
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:961-970
Online learning with kernel losses
Niladri Chatterji, Aldo Pacchiano, Peter Bartlett; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:971-980
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:981-990
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:991-1000
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
George Chen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1001-1010
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oates; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1011-1021
Particle Flow Bayes’ Rule
Xinshi Chen, Hanjun Dai, Le Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1022-1031
Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1032-1041
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Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1042-1051
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1052-1061
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1062-1070
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1071-1080
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1081-1090
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Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1091-1101
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1102-1111
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1112-1121
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Robust Decision Trees Against Adversarial Examples
Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1122-1131
RaFM: Rank-Aware Factorization Machines
Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1132-1140
Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1141-1150
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1151-1161
Variational Inference for sparse network reconstruction from count data
Julien Chiquet, Stephane Robin, Mahendra Mariadassou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1162-1171
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights
Uthsav Chitra, Benjamin Raphael; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1172-1181
Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1182-1192
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1193-1202
Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1203-1212
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Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
Casey Chu, Jose Blanchet, Peter Glynn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1213-1222
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
Eric Chu, Peter Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1223-1232
Weak Detection of Signal in the Spiked Wigner Model
Hye Won Chung, Ji Oon Lee; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1233-1241
New results on information theoretic clustering
Ferdinando Cicalese, Eduardo Laber, Lucas Murtinho; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1242-1251
Sensitivity Analysis of Linear Structural Causal Models
Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1252-1261
Dimensionality Reduction for Tukey Regression
Kenneth Clarkson, Ruosong Wang, David Woodruff; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1262-1271
On Medians of (Randomized) Pairwise Means
Pierre Laforgue, Stephan Clemencon, Patrice Bertail; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1272-1281
Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1282-1289
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
Eldan Cohen, Christopher Beck; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1290-1299
Learning Linear-Quadratic Regulators Efficiently with only $\sqrtT$ Regret
Alon Cohen, Tomer Koren, Yishay Mansour; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1300-1309
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Certified Adversarial Robustness via Randomized Smoothing
Jeremy Cohen, Elan Rosenfeld, Zico Kolter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1310-1320
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1321-1330
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1331-1340
A fully differentiable beam search decoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1341-1350
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Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucet; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1351-1360
Adjustment Criteria for Generalizing Experimental Findings
Juan Correa, Jin Tian, Elias Bareinboim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1361-1369
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Online Learning with Sleeping Experts and Feedback Graphs
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Scott Yang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1370-1378
Active Learning with Disagreement Graphs
Corinna Cortes, Giulia Desalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1379-1387
Shape Constraints for Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1388-1396
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1397-1405
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1406-1415
Boosted Density Estimation Remastered
Zac Cranko, Richard Nock; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1416-1425
Submodular Cost Submodular Cover with an Approximate Oracle
Victoria Crawford, Alan Kuhnle, My Thai; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1426-1435
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1436-1445
Anytime Online-to-Batch, Optimism and Acceleration
Ashok Cutkosky; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1446-1454
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Matrix-Free Preconditioning in Online Learning
Ashok Cutkosky, Tamas Sarlos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1455-1464
Minimal Achievable Sufficient Statistic Learning
Milan Cvitkovic, Günther Koliander; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1465-1474
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Milan Cvitkovic, Badal Singh, Animashree Anandkumar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1475-1485
The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1486-1495
Bayesian Optimization Meets Bayesian Optimal Stopping
Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1496-1506
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1507-1516
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Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Re; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1517-1527
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1528-1537
TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1538-1546
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Teaching a black-box learner
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1547-1555
Stochastic Deep Networks
Gwendoline De Bie, Gabriel Peyré, Marco Cuturi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1556-1565
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Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1566-1575
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
Onur Dereli, Ceyda Oğuz, Mehmet Gönen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1576-1585
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Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu, Daniel Worrall; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1586-1595
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1596-1606
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1607-1616
Noisy Dual Principal Component Pursuit
Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Rene Vidal, Manolis Tsakiris, Daniel Robinson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1617-1625
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Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
Thinh Doan, Siva Maguluri, Justin Romberg; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1626-1635
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Daniel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1636-1645
Generalized No Free Lunch Theorem for Adversarial Robustness
Elvis Dohmatob; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1646-1654
Width Provably Matters in Optimization for Deep Linear Neural Networks
Simon Du, Wei Hu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1655-1664
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1665-1674
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon Du, Jason Lee, Haochuan Li, Liwei Wang, Xiyu Zhai; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1675-1685
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
Junliang Du, Antonio Linero; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1686-1695
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du, Karthic Narasimhan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1696-1705
Optimal Auctions through Deep Learning
Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1706-1715
Wasserstein of Wasserstein Loss for Learning Generative Models
Yonatan Dukler, Wuchen Li, Alex Lin, Guido Montufar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1716-1725
Learning interpretable continuous-time models of latent stochastic dynamical systems
Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1726-1734
Autoregressive Energy Machines
Charlie Nash, Conor Durkan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1735-1744
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1745-1754
Imitating Latent Policies from Observation
Ashley Edwards, Himanshu Sahni, Yannick Schroecker, Charles Isbell; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1755-1763
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Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1764-1773
GDPP: Learning Diverse Generations using Determinantal Point Processes
Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1774-1783
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
Ehsan Elhamifar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1784-1793
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Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares
Alina Ene, Adrian Vladu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1794-1801
Exploring the Landscape of Spatial Robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1802-1811
Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1812-1822
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schoenlieb; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1823-1832
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1833-1842
Multi-Frequency Vector Diffusion Maps
Yifeng Fan, Zhizhen Zhao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1843-1852
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Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1853-1862
Regret Circuits: Composability of Regret Minimizers
Gabriele Farina, Christian Kroer, Tuomas Sandholm; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1863-1872
Dead-ends and Secure Exploration in Reinforcement Learning
Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen, Samira Ebrahimi Kahou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1873-1881
Invariant-Equivariant Representation Learning for Multi-Class Data
Ilya Feige; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1882-1891
The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1892-1900
Decentralized Exploration in Multi-Armed Bandits
Raphael Feraud, Reda Alami, Romain Laroche; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1901-1909
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1910-1919
Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1920-1930
DL2: Training and Querying Neural Networks with Logic
Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1931-1941
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1942-1951
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1952-1962
On discriminative learning of prediction uncertainty
Vojtech Franc, Daniel Prusa; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1963-1971
Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1972-1982
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1983-1992
Approximating Orthogonal Matrices with Effective Givens Factorization
Thomas Frerix, Joan Bruna; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1993-2001
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Fast and Flexible Inference of Joint Distributions from their Marginals
Charlie Frogner, Tomaso Poggio; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2002-2011
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2012-2020
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2021-2030
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2031-2041
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Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Kaito Fujii, Shinsaku Sakaue; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2042-2051
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2052-2062
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Shani Gamrian, Yoav Goldberg; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2063-2072
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian Ganea, Sylvain Gelly, Gary Becigneul, Aliaksei Severyn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2073-2082
Graph U-Nets
Hongyang Gao, Shuiwang Ji; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2083-2092
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Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2093-2101
Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2102-2111
Demystifying Dropout
Hongchang Gao, Jian Pei, Heng Huang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2112-2121
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Geometric Scattering for Graph Data Analysis
Feng Gao, Guy Wolf, Matthew Hirn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2122-2131
Multi-Frequency Phase Synchronization
Tingran Gao, Zhizhen Zhao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2132-2141
Optimal Mini-Batch and Step Sizes for SAGA
Nidham Gazagnadou, Robert Gower, Joseph Salmon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2142-2150
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman, Ran El-Yaniv; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2151-2159
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A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2160-2169
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2170-2179
Partially Linear Additive Gaussian Graphical Models
Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2180-2190
Learning and Data Selection in Big Datasets
Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2191-2200
Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2201-2210
Recursive Sketches for Modular Deep Learning
Badih Ghazi, Rina Panigrahy, Joshua Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2211-2220
An Instability in Variational Inference for Topic Models
Behrooz Ghorbani, Hamid Javadi, Andrea Montanari; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2221-2231
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani, Shankar Krishnan, Ying Xiao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2232-2241
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani, James Zou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2242-2251
Efficient Dictionary Learning with Gradient Descent
Dar Gilboa, Sam Buchanan, John Wright; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2252-2259
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2260-2268
Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2269-2279
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Adversarial Examples Are a Natural Consequence of Test Error in Noise
Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2280-2289
Discovering Conditionally Salient Features with Statistical Guarantees
Jaime Roquero Gimenez, James Zou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2290-2298
Estimating Information Flow in Deep Neural Networks
Ziv Goldfeld, Ewout Van Den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2299-2308
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Amortized Monte Carlo Integration
Adam Golinski, Frank Wood, Tom Rainforth; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2309-2318
Online Algorithms for Rent-Or-Buy with Expert Advice
Sreenivas Gollapudi, Debmalya Panigrahi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2319-2327
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The information-theoretic value of unlabeled data in semi-supervised learning
Alexander Golovnev, David Pal, Balazs Szorenyi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2328-2336
Efficient Training of BERT by Progressively Stacking
Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tieyan Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2337-2346
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Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization
Chengyue Gong, Jian Peng, Qiang Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2347-2356
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Obtaining Fairness using Optimal Transport Theory
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2357-2365
Combining parametric and nonparametric models for off-policy evaluation
Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2366-2375
Counterfactual Visual Explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2376-2384
Adaptive Sensor Placement for Continuous Spaces
James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili, Enrique Munoz De Cote; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2385-2393
A Statistical Investigation of Long Memory in Language and Music
Alexander Greaves-Tunnell, Zaid Harchaoui; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2394-2403
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Automatic Posterior Transformation for Likelihood-Free Inference
David Greenberg, Marcel Nonnenmacher, Jakob Macke; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2404-2414
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2415-2423
Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2424-2433
Graphite: Iterative Generative Modeling of Graphs
Aditya Grover, Aaron Zweig, Stefano Ermon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2434-2444
Fast Algorithm for Generalized Multinomial Models with Ranking Data
Jiaqi Gu, Guosheng Yin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2445-2453
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Towards a Deep and Unified Understanding of Deep Neural Models in NLP
Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2454-2463
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An Investigation of Model-Free Planning
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2464-2473
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2474-2483
Simple Black-box Adversarial Attacks
Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2484-2493
Exploring interpretable LSTM neural networks over multi-variable data
Tian Guo, Tao Lin, Nino Antulov-Fantulin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2494-2504
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Lingbing Guo, Zequn Sun, Wei Hu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2505-2514
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Albert Gural, Boris Murmann; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2515-2524
IMEXnet A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2525-2534
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On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen, Daphna Weinshall; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2535-2544
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2545-2554
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2555-2565
Neural Separation of Observed and Unobserved Distributions
Tavi Halperin, Ariel Ephrat, Yedid Hoshen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2566-2575
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Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2576-2585
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Seungyul Han, Youngchul Sung; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2586-2595
Complexity of Linear Regions in Deep Networks
Boris Hanin, David Rolnick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2596-2604
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Josiah Hanna, Scott Niekum, Peter Stone; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2605-2613
Doubly-Competitive Distribution Estimation
Yi Hao, Alon Orlitsky; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2614-2623
Random Shuffling Beats SGD after Finite Epochs
Jeff Haochen, Suvrit Sra; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2624-2633
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2634-2643
Per-Decision Option Discounting
Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowe, Doina Precup; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2644-2652
Submodular Observation Selection and Information Gathering for Quadratic Models
Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2653-2662
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv, Alexander Rivkind, Omri Barak; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2663-2671
On the Impact of the Activation function on Deep Neural Networks Training
Soufiane Hayou, Arnaud Doucet, Judith Rousseau; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2672-2680
Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2681-2691
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari, Vedant Nanda, Krishna Gummadi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2692-2701
Graph Resistance and Learning from Pairwise Comparisons
Julien Hendrickx, Alexander Olshevsky, Venkatesh Saligrama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2702-2711
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Kimin Lee, Mantas Mazeika; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2712-2721
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2722-2730
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2731-2741
Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2742-2750
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2751-2760
Better generalization with less data using robust gradient descent
Matthew Holland, Kazushi Ikeda; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2761-2770
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne Van Den Berg, Max Welling; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2771-2780
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Nonconvex Variance Reduced Optimization with Arbitrary Sampling
Samuel Horváth, Peter Richtarik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2781-2789
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2790-2799
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P R Kumar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2800-2809
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Ya-Ping Hsieh, Chen Liu, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2810-2819
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2820-2829
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu, Fabio Ramos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2830-2838
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang, Songcan Chen, Heng Huang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2839-2848
Unsupervised Deep Learning by Neighbourhood Discovery
Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2849-2858
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Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm
Kejun Huang, Xiao Fu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2859-2868
Hierarchical Importance Weighted Autoencoders
Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2869-2878
Stable and Fair Classification
Lingxiao Huang, Nisheeth Vishnoi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2879-2890
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Bautista Martin, Shih-Yu Sun, Carlos Guestrin, Josh Susskind; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2891-2900
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2901-2910
Composing Entropic Policies using Divergence Correction
Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2911-2920
HexaGAN: Generative Adversarial Nets for Real World Classification
Uiwon Hwang, Dahuin Jung, Sungroh Yoon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2921-2930
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2931-2940
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Learning Structured Decision Problems with Unawareness
Craig Innes, Alex Lascarides; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2941-2950
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Niels Ipsen, Lars Kai Hansen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2951-2960
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Shariq Iqbal, Fei Sha; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2961-2970
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2971-2980
Causal Identification under Markov Equivalence: Completeness Results
Amin Jaber, Jiji Zhang, Elias Bareinboim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2981-2989
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Learning from a Learner
Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2990-2999
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi -Malvajerdi, Jonathan Ullman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3000-3008
Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3009-3018
DBSCAN++: Towards fast and scalable density clustering
Jennifer Jang, Heinrich Jiang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3019-3029
Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3030-3039
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z. Leibo, Nando De Freitas; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3040-3049
A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3050-3059
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Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3060-3070
Ladder Capsule Network
Taewon Jeong, Youngmin Lee, Heeyoung Kim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3071-3079
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Training CNNs with Selective Allocation of Channels
Jongheon Jeong, Jinwoo Shin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3080-3090
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Yeonwoo Jeong, Hyun Oh Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3091-3099
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3100-3109
Neural Logic Reinforcement Learning
Zhengyao Jiang, Shan Luo; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3110-3119
Finding Options that Minimize Planning Time
Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3120-3129
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai, Jee Won Park, David Abel, George Konidaris; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3130-3139
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Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3140-3151
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
David John, Vincent Heuveline, Michael Schober; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3152-3162
Bilinear Bandits with Low-rank Structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3163-3172
Statistical Foundations of Virtual Democracy
Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3173-3182
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Molecular Hypergraph Grammar with Its Application to Molecular Optimization
Hiroshi Kajino; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3183-3191
Robust Influence Maximization for Hyperparametric Models
Dimitris Kalimeris, Gal Kaplun, Yaron Singer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3192-3200
Classifying Treatment Responders Under Causal Effect Monotonicity
Nathan Kallus; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3201-3210
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Trainable Decoding of Sets of Sequences for Neural Sequence Models
Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3211-3221
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3222-3232
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3233-3241
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Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis, Murray Shanahan, Claudia Clopath; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3242-3251
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3252-3261
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3262-3271
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Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3272-3281
Processing Megapixel Images with Deep Attention-Sampling Models
Angelos Katharopoulos, Francois Fleuret; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3282-3291
Robust Estimation of Tree Structured Gaussian Graphical Models
Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3292-3300
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3301-3310
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Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3311-3320
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3321-3330
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3331-3340
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Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3341-3350
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
Renata Khasanova, Pascal Frossard; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3351-3359
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EMI: Exploration with Mutual Information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3360-3369
FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3370-3378
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Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3379-3388
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
Gi-Soo Kim, Myunghee Cho Paik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3389-3397
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry Wasserman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3398-3407
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
Friso Kingma, Pieter Abbeel, Jonathan Ho; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3408-3417
CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3418-3428
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3429-3438
AUCμ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman, David Page; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3439-3447
Fair k-Center Clustering for Data Summarization
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3448-3457
Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3458-3467
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3468-3477
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian Stich, Martin Jaggi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3478-3487
Robust Learning from Untrusted Sources
Nikola Konstantinov, Christoph Lampert; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3488-3498
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke Van Hoof, Max Welling; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3499-3508
LIT: Learned Intermediate Representation Training for Model Compression
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3509-3518
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Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3519-3529
On the Complexity of Approximating Wasserstein Barycenters
Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3530-3540
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
Andrei Kulunchakov, Julien Mairal; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3541-3550
Faster Algorithms for Binary Matrix Factorization
Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3551-3559
Loss Landscapes of Regularized Linear Autoencoders
Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva, Cotton Seed; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3560-3569
Geometry and Symmetry in Short-and-Sparse Deconvolution
Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3570-3580
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3581-3590
Making Decisions that Reduce Discriminatory Impacts
Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3591-3600
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3601-3610
Characterizing Well-Behaved vs. Pathological Deep Neural Networks
Antoine Labatie; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3611-3621
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3622-3631
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A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion
Sylvain Lamprier; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3632-3641
Projection onto Minkowski Sums with Application to Constrained Learning
Joong-Ho Won, Jason Xu, Kenneth Lange; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3642-3651
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Remi Tachet Des Combes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3652-3661
A Better k-means++ Algorithm via Local Search
Silvio Lattanzi, Christian Sohler; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3662-3671
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Lorentzian Distance Learning for Hyperbolic Representations
Marc Law, Renjie Liao, Jake Snell, Richard Zemel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3672-3681
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Andrew Lawrence, Carl Henrik Ek, Neill Campbell; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3682-3691
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction
Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, Gellert Weisz; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3692-3702
Batch Policy Learning under Constraints
Hoang Le, Cameron Voloshin, Yisong Yue; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3703-3712
Target-Based Temporal-Difference Learning
Donghwan Lee, Niao He; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3713-3722
Functional Transparency for Structured Data: a Game-Theoretic Approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3723-3733
Self-Attention Graph Pooling
Junhyun Lee, Inyeop Lee, Jaewoo Kang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3734-3743
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Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, Yee Whye Teh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3744-3753
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems
Ching-Pei Lee, Stephen Wright; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3754-3762
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3763-3772
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
Yifan Lei, Qiang Huang, Mohan Kankanhalli, Anthony Tung; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3773-3781
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle, Zoltan Szabo, Timothée Mathieu, Guillaume Lecue; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3782-3793
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
Mario Lezcano-Casado, David Martı́nez-Rubio; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3794-3803
Are Generative Classifiers More Robust to Adversarial Attacks?
Yingzhen Li, John Bradshaw, Yash Sharma; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3804-3814
Sublinear quantum algorithms for training linear and kernel-based classifiers
Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3815-3824
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3825-3834
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Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3835-3845
Area Attention
Yang Li, Lukasz Kaiser, Samy Bengio, Si Si; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3846-3855
Online Learning to Rank with Features
Shuai Li, Tor Lattimore, Csaba Szepesvari; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3856-3865
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3866-3876
Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Li, Tyler Mccormick, Samuel Clark; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3877-3885
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing
Yuan Li, Benjamin Rubinstein, Trevor Cohn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3886-3895
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Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li, Frank Schmidt, Zico Kolter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3896-3904
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Towards a Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3905-3914
Feature-Critic Networks for Heterogeneous Domain Generalization
Yiying Li, Yongxin Yang, Wei Zhou, Timothy Hospedales; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3915-3924
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Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3925-3934
Alternating Minimizations Converge to Second-Order Optimal Solutions
Qiuwei Li, Zhihui Zhu, Gongguo Tang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3935-3943
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Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints
Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3944-3952
Regularization in directable environments with application to Tetris
Jan Malte Lichtenberg, Özgür Şimşek; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3953-3962
Inference and Sampling of $K_33$-free Ising Models
Valerii Likhosherstov, Yury Maximov, Misha Chertkov; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3963-3972
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes
Shiau Hong Lim, Arnaud Autef; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3973-3981
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On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
Tianyi Lin, Nhat Ho, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3982-3991
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3992-4002
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Yanli Liu, Fei Feng, Wotao Yin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4003-4012
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4013-4022
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Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, Jon Mcauliffe; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4023-4031
Sparse Extreme Multi-label Learning with Oracle Property
Weiwei Liu, Xiaobo Shen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4032-4041
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Data Poisoning Attacks on Stochastic Bandits
Fang Liu, Ness Shroff; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4042-4050
The Implicit Fairness Criterion of Unconstrained Learning
Lydia T. Liu, Max Simchowitz, Moritz Hardt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4051-4060
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Taming MAML: Efficient unbiased meta-reinforcement learning
Hao Liu, Richard Socher, Caiming Xiong; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4061-4071
On Certifying Non-Uniform Bounds against Adversarial Attacks
Chen Liu, Ryota Tomioka, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4072-4081
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4082-4092
Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu, Jingwei Zhuo, Jun Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4093-4103
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4104-4113
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4114-4124
Bayesian Counterfactual Risk Minimization
Ben London, Ted Sandler; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4125-4133
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
Songtao Lu, Mingyi Hong, Zhengdao Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4134-4143
Neurally-Guided Structure Inference
Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4144-4153
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4154-4163
CoT: Cooperative Training for Generative Modeling of Discrete Data
Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4164-4172
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Carlo Lucibello, Luca Saglietti, Yue Lu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4173-4182
High-Fidelity Image Generation With Fewer Labels
Mario Lučić, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4183-4192
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4193-4202
Differentiable Dynamic Normalization for Learning Deep Representation
Ping Luo, Peng Zhanglin, Shao Wenqi, Zhang Ruimao, Ren Jiamin, Wu Lingyun; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4203-4211
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Disentangled Graph Convolutional Networks
Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4212-4221
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Variational Implicit Processes
Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4222-4233
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4234-4243
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4244-4253
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4254-4263
Guided evolutionary strategies: augmenting random search with surrogate gradients
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4264-4273
Universal Multi-Party Poisoning Attacks
Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4274-4283
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Traditional and Heavy Tailed Self Regularization in Neural Network Models
Michael Mahoney, Charles Martin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4284-4293
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Curvature-Exploiting Acceleration of Elastic Net Computations
Vien Mai, Mikael Johansson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4294-4303
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms
Ashok Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4304-4313
Calibrated Model-Based Deep Reinforcement Learning
Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4314-4323
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Timothy Arthur Mann, Sven Gowal, Andras Gyorgy, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4324-4332
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4333-4342
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
Jingkai Mao, Jakob Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4343-4351
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Andrés Marafioti, Nathanaël Perraudin, Nicki Holighaus, Piotr Majdak; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4352-4362
On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4363-4371
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens, Kieran Campbell, Christopher Yau; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4372-4381
Fairness-Aware Learning for Continuous Attributes and Treatments
Jeremie Mary, Clément Calauzènes, Noureddine El Karoui; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4382-4391
Optimal Minimal Margin Maximization with Boosting
Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4392-4401
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Disentangling Disentanglement in Variational Autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4402-4412
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
Pierre-Alexandre Mattei, Jes Frellsen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4413-4423
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4424-4434
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Graphical-model based estimation and inference for differential privacy
Ryan Mckenna, Daniel Sheldon, Gerome Miklau; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4435-4444
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Edward Meeds; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4445-4455
Toward Controlling Discrimination in Online Ad Auctions
Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4456-4465
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin Duke, Piyush Rai; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4466-4474
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei, Guanghui Qin, Jason Eisner; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4475-4485
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4486-4495
The Wasserstein Transform
Facundo Memoli, Zane Smith, Zhengchao Wan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4496-4504
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Dr.Saman Amarasinghe, Michael Carbin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4505-4515
Geometric Losses for Distributional Learning
Arthur Mensch, Mathieu Blondel, Gabriel Peyré; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4516-4525
Spectral Clustering of Signed Graphs via Matrix Power Means
Pedro Mercado, Francesco Tudisco, Matthias Hein; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4526-4536
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Michael Metel, Akiko Takeda; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4537-4545
Reinforcement Learning in Configurable Continuous Environments
Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4546-4555
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4556-4565
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer, Jean Honorio; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4566-4574
On Dropout and Nuclear Norm Regularization
Poorya Mianjy, Raman Arora; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4575-4584
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
Andrew Miller, Ziad Obermeyer, John Cunningham, Sendhil Mullainathan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4585-4594
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavković; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4595-4604
Co-manifold learning with missing data
Gal Mishne, Eric Chi, Ronald Coifman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4605-4614
Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4615-4625
Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff, Daniel Cremers; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4626-4635
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon, Gaon An, Hyun Oh Song; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4636-4645
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa, Xin Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4646-4655
A Dynamical Systems Perspective on Nesterov Acceleration
Michael Muehlebach, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4656-4662
Relational Pooling for Graph Representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4663-4673
Learning Optimal Fair Policies
Razieh Nabi, Daniel Malinsky, Ilya Shpitser; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4674-4682
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee, Nathan Srebro, Daniel Soudry; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4683-4692
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4693-4702
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4703-4711
Dropout as a Structured Shrinkage Prior
Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4712-4722
Hybrid Models with Deep and Invertible Features
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4723-4732
Learning Context-dependent Label Permutations for Multi-label Classification
Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4733-4742
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Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4743-4751
A Framework for Bayesian Optimization in Embedded Subspaces
Amin Nayebi, Alexander Munteanu, Matthias Poloczek; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4752-4761
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4762-4770
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Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4771-4780
Learning to bid in revenue-maximizing auctions
Thomas Nedelec, Noureddine El Karoui, Vianney Perchet; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4781-4789
On Connected Sublevel Sets in Deep Learning
Quynh Nguyen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4790-4799
Anomaly Detection With Multiple-Hypotheses Predictions
Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4800-4809
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
Than Huy Nguyen, Umut Simsekli, Gael Richard; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4810-4819
Rotation Invariant Householder Parameterization for Bayesian PCA
Rajbir Nirwan, Nils Bertschinger; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4820-4828
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Lossless or Quantized Boosting with Integer Arithmetic
Richard Nock, Robert Williamson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4829-4838
Training Neural Networks with Local Error Signals
Arild Nøkland, Lars Hiller Eidnes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4839-4850
Remember and Forget for Experience Replay
Guido Novati, Petros Koumoutsakos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4851-4860
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Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4861-4870
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4871-4880
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Michael Oberst, David Sontag; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4881-4890
Model Function Based Conditional Gradient Method with Armijo-like Line Search
Peter Ochs, Yura Malitsky; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4891-4900
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4901-4911
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Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gärtner; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4912-4921
Approximation and non-parametric estimation of ResNet-type convolutional neural networks
Kenta Oono, Taiji Suzuki; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4922-4931
Orthogonal Random Forest for Causal Inference
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4932-4941
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama, Dave Zachariah, Thomas B. Schön; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4942-4950
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Samet Oymak, Mahdi Soltanolkotabi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4951-4960
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas, Georgios Piliouras, Xiao Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4961-4969
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4970-4979
Nonparametric Bayesian Deep Networks with Local Competition
Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4980-4988
Optimistic Policy Optimization via Multiple Importance Sampling
Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4989-4999
Deep Residual Output Layers for Neural Language Generation
Nikolaos Pappas, James Henderson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5000-5011
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Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
Vardan Papyan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5012-5021
Generalized Majorization-Minimization
Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5022-5031
Variational Laplace Autoencoders
Yookoon Park, Chris Kim, Gunhee Kim; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5032-5041
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel Park, Jascha Sohl-Dickstein, Quoc Le, Samuel Smith; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5042-5051
Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5052-5061
Self-Supervised Exploration via Disagreement
Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5062-5071
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Subspace Robust Wasserstein Distances
François-Pierre Paty, Marco Cuturi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5072-5081
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Supratik Paul, Michael A. Osborne, Shimon Whiteson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5082-5091
COMIC: Multi-view Clustering Without Parameter Selection
Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5092-5101
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Domain Agnostic Learning with Disentangled Representations
Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5102-5112
Collaborative Channel Pruning for Deep Networks
Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5113-5122
Exploiting structure of uncertainty for efficient matroid semi-bandits
Pierre Perrault, Vianney Perchet, Michal Valko; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5123-5132
Cognitive model priors for predicting human decisions
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5133-5141
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Towards Understanding Knowledge Distillation
Mary Phuong, Christoph Lampert; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5142-5151
Temporal Gaussian Mixture Layer for Videos
Aj Piergiovanni, Michael Ryoo; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5152-5161
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
Vladislav Polianskii, Florian T. Pokorny; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5162-5170
On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, George Tucker; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5171-5180
Hiring Under Uncertainty
Manish Purohit, Sreenivas Gollapudi, Manish Raghavan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5181-5189
SAGA with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5190-5199
SGD: General Analysis and Improved Rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5200-5209
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5210-5219
Fault Tolerance in Iterative-Convergent Machine Learning
Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric Xing; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5220-5230
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5231-5240
GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian Tang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5241-5250
Nonlinear Distributional Gradient Temporal-Difference Learning
Chao Qu, Shie Mannor, Huan Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5251-5260
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Learning to Collaborate in Markov Decision Processes
Goran Radanovic, Rati Devidze, David Parkes, Adish Singla; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5261-5270
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Meta-Learning Neural Bloom Filters
Jack Rae, Sergey Bartunov, Timothy Lillicrap; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5271-5280
Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, Jon Kleinberg; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5281-5290
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
Arvind Raghunathan, Anoop Cherian, Devesh Jha; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5291-5300
On the Spectral Bias of Neural Networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5301-5310
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
Tahrima Rahman, Shasha Jin, Vibhav Gogate; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5311-5320
Does Data Augmentation Lead to Positive Margin?
Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5321-5330
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5331-5340
Screening rules for Lasso with non-convex Sparse Regularizers
Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5341-5350
Topological Data Analysis of Decision Boundaries with Application to Model Selection
Karthikeyan Natesan Ramamurthy, Kush Varshney, Krishnan Mody; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5351-5360
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff, Li Fuxin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5361-5369
Efficient On-Device Models using Neural Projections
Sujith Ravi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5370-5379
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Ramin Raziperchikolaei, Harish Bhat; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5380-5388
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5389-5400
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
Henry Reeve, Ata Kaban; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5401-5409
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5410-5419
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Adaptive Antithetic Sampling for Variance Reduction
Hongyu Ren, Shengjia Zhao, Stefano Ermon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5420-5428
Adversarial Online Learning with noise
Alon Resler, Yishay Mansour; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5429-5437
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
Alireza Rezaei, Shayan Oveis Gharan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5438-5447
A Persistent Weisfeiler-Lehman Procedure for Graph Classification
Bastian Rieck, Christian Bock, Karsten Borgwardt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5448-5458
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5459-5467
Separating value functions across time-scales
Joshua Romoff, Peter Henderson, Ahmed Touati, Emma Brunskill, Joelle Pineau, Yann Ollivier; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5468-5477
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5478-5486
Good Initializations of Variational Bayes for Deep Models
Simone Rossi, Pietro Michiardi, Maurizio Filippone; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5487-5497
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth, Yannic Kilcher, Thomas Hofmann; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5498-5507
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5508-5517
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy, Zaid Harchaoui; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5518-5527
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Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5528-5536
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco Ruiz, Michalis Titsias; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5537-5545
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5546-5557
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Herve Jegou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5558-5567
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
Touqir Sajed, Or Sheffet; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5579-5588
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Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Deisenroth; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5589-5598
Multivariate Submodular Optimization
Richard Santiago, F. Bruce Shepherd; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5599-5609
Near optimal finite time identification of arbitrary linear dynamical systems
Tuhin Sarkar, Alexander Rakhlin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5610-5618
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5619-5627
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5628-5637
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5638-5648
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter, Kirill Sidorov, David Marshall; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5649-5659
Discovering Context Effects from Raw Choice Data
Arjun Seshadri, Alex Peysakhovich, Johan Ugander; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5660-5669
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca Dragan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5670-5679
Exploration Conscious Reinforcement Learning Revisited
Lior Shani, Yonathan Efroni, Shie Mannor; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5680-5689
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5690-5700
Conditional Independence in Testing Bayesian Networks
Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5701-5709
Learning to Clear the Market
Weiran Shen, Sebastien Lahaie, Renato Paes Leme; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5710-5718
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Mixture Models for Diverse Machine Translation: Tricks of the Trade
Tianxiao Shen, Myle Ott, Michael Auli, Marc’Aurelio Ranzato; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5719-5728
Hessian Aided Policy Gradient
Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5729-5738
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
Yanyao Shen, Sujay Sanghavi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5739-5748
Replica Conditional Sequential Monte Carlo
Alex Shestopaloff, Arnaud Doucet; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5749-5757
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5758-5768
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning
Weishi Shi, Qi Yu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5769-5778
Model-Based Active Exploration
Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5779-5788
Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5789-5798
Revisiting precision recall definition for generative modeling
Loic Simon, Ryan Webster, Julien Rabin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5799-5808
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First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, David Lopez-Paz; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5809-5817
Refined Complexity of PCA with Outliers
Kirill Simonov, Fedor Fomin, Petr Golovach, Fahad Panolan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5818-5826
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A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5827-5837
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Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin Braun; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5838-5847
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5848-5856
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim, Clément Chatelain, Chloe-Agathe Azencott, Jean-Philippe Vert; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5857-5865
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Edward Smith, Scott Fujimoto, Adriana Romero, David Meger; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5866-5876
The Evolved Transformer
David So, Quoc Le, Chen Liang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5877-5886
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5887-5896
Distribution calibration for regression
Hao Song, Tom Diethe, Meelis Kull, Peter Flach; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5897-5906
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SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
Hwanjun Song, Minseok Kim, Jae-Gil Lee; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5907-5915
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song, Ron Parr, Lawrence Carin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5916-5925
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5926-5936
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Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Pedro Soto, Jun Li, Xiaodi Fan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5937-5945
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Compressing Gradient Optimizers via Count-Sketches
Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5946-5955
Escaping Saddle Points with Adaptive Gradient Methods
Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5956-5965
Faster Attend-Infer-Repeat with Tractable Probabilistic Models
Karl Stelzner, Robert Peharz, Kristian Kersting; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5966-5975
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5976-5985
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BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Asa Cooper Stickland, Iain Murray; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5986-5995
Learning Optimal Linear Regularizers
Matthew Streeter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:5996-6004
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning
Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6005-6014
Learning Distance for Sequences by Learning a Ground Metric
Bing Su, Ying Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6015-6025
Contextual Memory Trees
Wen Sun, Alina Beygelzimer, Hal Daumé Iii, John Langford, Paul Mineiro; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6026-6035
Provably Efficient Imitation Learning from Observation Alone
Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6036-6045
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6046-6055
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, Stefan Bauer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6056-6065
Hyperbolic Disk Embeddings for Directed Acyclic Graphs
Ryota Suzuki, Ryusuke Takahama, Shun Onoda; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6066-6075
Accelerated Flow for Probability Distributions
Amirhossein Taghvaei, Prashant Mehta; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6076-6085
Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, Gregory Valiant; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6086-6095
Making Deep Q-learning methods robust to time discretization
Corentin Tallec, Léonard Blier, Yann Ollivier; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6096-6104
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan, Quoc Le; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6105-6114
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Hierarchical Decompositional Mixtures of Variational Autoencoders
Ping Liang Tan, Robert Peharz; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6115-6124
Mallows ranking models: maximum likelihood estimate and regeneration
Wenpin Tang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6125-6134
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Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6135-6144
The Variational Predictive Natural Gradient
Da Tang, Rajesh Ranganath; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6145-6154
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression
Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6155-6165
Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6166-6175
Variational Annealing of GANs: A Langevin Perspective
Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy Bobashev, Lawrence Carin Duke; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6176-6185
Predicate Exchange: Inference with Declarative Knowledge
Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6186-6195
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The Natural Language of Actions
Guy Tennenholtz, Shie Mannor; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6196-6205
Kernel Normalized Cut: a Theoretical Revisit
Yoshikazu Terada, Michio Yamamoto; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6206-6214
Action Robust Reinforcement Learning and Applications in Continuous Control
Chen Tessler, Yonathan Efroni, Shie Mannor; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6215-6224
Concentration Inequalities for Conditional Value at Risk
Philip Thomas, Erik Learned-Miller; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6225-6233
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Combating Label Noise in Deep Learning using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6234-6243
ELF OpenGo: an analysis and open reimplementation of AlphaZero
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6244-6253
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6254-6263
Transfer of Samples in Policy Search via Multiple Importance Sampling
Andrea Tirinzoni, Mattia Salvini, Marcello Restelli; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6264-6274
Optimal Transport for structured data with application on graphs
Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, Rémi Flamary; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6275-6284
Discovering Latent Covariance Structures for Multiple Time Series
Anh Tong, Jaesik Choi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6285-6294
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6295-6304
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DeepNose: Using artificial neural networks to represent the space of odorants
Ngoc Tran, Daniel Kepple, Sergey Shuvaev, Alexei Koulakov; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6305-6314
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LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6315-6324
Learning Hawkes Processes Under Synchronization Noise
William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6325-6334
Homomorphic Sensing
Manolis Tsakiris, Liangzu Peng; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6335-6344
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Metropolis-Hastings Generative Adversarial Networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6345-6353
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Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
Ruo-Chun Tzeng, Shan-Hung Wu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6354-6362
Sublinear Space Private Algorithms Under the Sliding Window Model
Jalaj Upadhyay; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6363-6372
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
Berk Ustun, Yang Liu, David Parkes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6373-6382
Large-Scale Sparse Kernel Canonical Correlation Analysis
Viivi Uurtio, Sahely Bhadra, Juho Rousu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6383-6391
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
Marten Van Dijk, Lam Nguyen, Phuong Ha Nguyen, Dzung Phan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6392-6400
Composing Value Functions in Reinforcement Learning
Benjamin Van Niekerk, Steven James, Adam Earle, Benjamin Rosman; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6401-6409
Model Comparison for Semantic Grouping
Francisco Vargas, Kamen Brestnichki, Nils Hammerla; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6410-6417
Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Re; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6418-6427
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6428-6437
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6438-6447
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6448-6457
Understanding Priors in Bayesian Neural Networks at the Unit Level
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6458-6467
On the Design of Estimators for Bandit Off-Policy Evaluation
Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou, Tony Jebara; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6468-6476
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Learning to select for a predefined ranking
Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev, Pavel Serdyukov; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6477-6486
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6487-6494
Graph Convolutional Gaussian Processes
Ian Walker, Ben Glocker; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6495-6504
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Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
Tong Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6505-6514
Convolutional Poisson Gamma Belief Network
Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6515-6525
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
Di Wang, Changyou Chen, Jinhui Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6526-6535
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, Yiannis Demiris; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6536-6544
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SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder, Zico Kolter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6545-6554
Improving Neural Language Modeling via Adversarial Training
Dilin Wang, Chengyue Gong, Qiang Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6555-6565
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EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6566-6575
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Dilin Wang, Qiang Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6576-6585
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6586-6595
State-Regularized Recurrent Neural Networks
Cheng Wang, Mathias Niepert; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6596-6606
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6607-6617
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio Calmon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6618-6627
On Sparse Linear Regression in the Local Differential Privacy Model
Di Wang, Jinhui Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6628-6637
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6638-6647
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6648-6658
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
Shengjie Wang, Tianyi Zhou, Jeff Bilmes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6659-6667
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang, Tianyi Zhou, Jeff Bilmes; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6668-6676
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AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes
Rachel Ward, Xiaoxia Wu, Leon Bottou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6677-6686
Generalized Linear Rule Models
Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Gunluk; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6687-6696
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
Xiaohan Wei, Zhuoran Yang, Zhaoran Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6697-6706
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellert Weisz, Andras Gyorgy, Csaba Szepesvari; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6707-6715
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé Iii, Kyunghyun Cho; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6716-6726
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, Luca Daniel; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6727-6736
Learning deep kernels for exponential family densities
Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6737-6746
Improving Model Selection by Employing the Test Data
Max Westphal, Werner Brannath; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6747-6756
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Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill, Anand Ramakrishnan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6757-6765
Moment-Based Variational Inference for Markov Jump Processes
Christian Wildner, Heinz Koeppl; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6766-6775
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6776-6785
Fairness risk measures
Robert Williamson, Aditya Menon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6786-6797
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6798-6807
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank Schmidt, Zico Kolter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6808-6817
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6818-6827
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6828-6839
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6840-6849
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Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6850-6860
Simplifying Graph Convolutional Networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6861-6871
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6872-6881
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6882-6892
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie, Sanmi Koyejo, Indranil Gupta; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6893-6901
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6902-6911
Calibrated Approximate Bayesian Inference
Hanwen Xing, Geoff Nicholls, Jeong Lee; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6912-6920
Power k-Means Clustering
Jason Xu, Kenneth Lange; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6921-6931
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Duke; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6932-6941
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6942-6951
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6952-6962
Variational Russian Roulette for Deep Bayesian Nonparametrics
Kai Xu, Akash Srivastava, Charles Sutton; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6963-6972
Supervised Hierarchical Clustering with Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallum; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6973-6983
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang, Jia Deng; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6984-6994
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features
Lin Yang, Mengdi Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6995-7004
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7005-7014
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SWALP : Stochastic Weight Averaging in Low Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Chris De Sa; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7015-7024
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7025-7034
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations
Quanming Yao, James Tin-Yau Kwok, Bo Han; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7035-7044
Hierarchically Structured Meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7045-7054
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering
Taisuke Yasuda, David Woodruff, Manuel Fernandez; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7055-7063
Understanding Geometry of Encoder-Decoder CNNs
Jong Chul Ye, Woon Kyoung Sung; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7064-7073
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7074-7084
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Ramchandran Kannan, Peter Bartlett; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7085-7094
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
Mingzhang Yin, Yuguang Yue, Mingyuan Zhou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7095-7104
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7105-7114
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon, Jun Seo, Jaekyun Moon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7115-7123
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7124-7133
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Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7134-7143
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Learning Neurosymbolic Generative Models via Program Synthesis
Halley Young, Osbert Bastani, Mayur Naik; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7144-7153
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao, Mo Yu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7154-7163
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, Masashi Sugiyama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7164-7173
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
Hao Yu, Rong Jin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7174-7183
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Hao Yu, Rong Jin, Sen Yang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7184-7193
Multi-Agent Adversarial Inverse Reinforcement Learning
Lantao Yu, Jiaming Song, Stefano Ermon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7194-7201
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7202-7212
Online Adaptive Principal Component Analysis and Its extensions
Jianjun Yuan, Andrew Lamperski; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7213-7221
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation
Jinyang Yuan, Bin Li, Xiangyang Xue; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7222-7231
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7232-7241
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning
Jihun Yun, Peng Zheng, Eunho Yang, Aurelie Lozano, Aleksandr Aravkin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7242-7251
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7252-7261
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyen; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7262-7271
A Conditional-Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever, Olivier Fercoq, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7272-7281
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Alp Yurtsever, Suvrit Sra, Volkan Cevher; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7282-7291
Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7292-7303
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Andrea Zanette, Emma Brunskill; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7304-7312
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Global Convergence of Block Coordinate Descent in Deep Learning
Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7313-7323
Making Convolutional Networks Shift-Invariant Again
Richard Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7324-7334
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Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé Iii, John Langford, Sahand Negahban; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7335-7344
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When Samples Are Strategically Selected
Hanrui Zhang, Yu Cheng, Vincent Conitzer; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7345-7353
Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7354-7363
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Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
Guo Zhang, Hao He, Dina Katabi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7364-7373
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition
Songyang Zhang, Xuming He, Shipeng Yan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7374-7383
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Neural Collaborative Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi, Wenbing Huang, Hongdong Li; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7384-7393
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Incremental Randomized Sketching for Online Kernel Learning
Xiao Zhang, Shizhong Liao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7394-7403
Bridging Theory and Algorithm for Domain Adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7404-7413
Adaptive Regret of Convex and Smooth Functions
Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7414-7423
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Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7424-7433
Co-Representation Network for Generalized Zero-Shot Learning
Fei Zhang, Guangming Shi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7434-7443
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7444-7453
A Composite Randomized Incremental Gradient Method
Junyu Zhang, Lin Xiao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7454-7462
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
Chenyang Zhang, Guosheng Yin; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7463-7471
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Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7472-7482
Learning Novel Policies For Tasks
Yunbo Zhang, Wenhao Yu, Greg Turk; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7483-7492
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization
Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7493-7501
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Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang, Zhanxing Zhu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7502-7511
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin Zhang, James Zou, David Tse; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7512-7522
On Learning Invariant Representations for Domain Adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordon; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7523-7532
Metric-Optimized Example Weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7533-7542
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, Zhiru Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7543-7552
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Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao, Xudong Sun, Volker Tresp; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7553-7562
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Baojian Zhou, Feng Chen, Yiming Ying; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7563-7573
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou, Quanquan Gu; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7574-7583
Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7584-7593
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Toward Understanding the Importance of Noise in Training Neural Networks
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7594-7602
BayesNAS: A Bayesian Approach for Neural Architecture Search
Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7603-7613
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7614-7623
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, Jinbo Bi; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7624-7633
Poission Subsampled Rényi Differential Privacy
Yuqing Zhu, Yu-Xiang Wang; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7634-7642
Learning Classifiers for Target Domain with Limited or No Labels
Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7643-7653
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The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7654-7663
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7664-7672
Latent Normalizing Flows for Discrete Sequences
Zachary Ziegler, Alexander Rush; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7673-7682
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert, Haipeng Luo, Chen-Yu Wei; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7683-7692
Fast Context Adaptation via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whiteson; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7693-7702
Natural Analysts in Adaptive Data Analysis
Tijana Zrnic, Moritz Hardt; Proceedings of the 36th International Conference on Machine Learning, PMLR 97:7703-7711
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