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ORCID38th ICML 2021: Virtual Event
- Marina Meila, Tong Zhang:
Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research 139, PMLR 2021 - Majid Abdolshah, Hung Le, Thommen George Karimpanal, Sunil Gupta, Santu Rana, Svetha Venkatesh:
A New Representation of Successor Features for Transfer across Dissimilar Environments. 1-9 - Kuruge Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Rahimi Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan Kumar Yadav:
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling. 10-20 - Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Debiasing Model Updates for Improving Personalized Federated Training. 21-31 - Durmus Alp Emre Acar, Ruizhao Zhu, Venkatesh Saligrama:
Memory Efficient Online Meta Learning. 32-42 - Jayadev Acharya, Ziteng Sun, Huanyu Zhang:
Robust Testing and Estimation under Manipulation Attacks. 43-53 - Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya:
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. 54-65 - David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler:
f-Domain Adversarial Learning: Theory and Algorithms. 66-75 - Darius Afchar, Vincent Guigue, Romain Hennequin:
Towards Rigorous Interpretations: a Formalisation of Feature Attribution. 76-86 - Naman Agarwal, Surbhi Goel, Cyril Zhang:
Acceleration via Fractal Learning Rate Schedules. 87-99 - Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh:
A Regret Minimization Approach to Iterative Learning Control. 100-109 - Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju:
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. 110-119 - Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier:
Label Inference Attacks from Log-loss Scores. 120-129 - Laurence Aitchison, Adam X. Yang, Sebastian W. Ober:
Deep Kernel Processes. 130-140 - Ali Akbari, Muhammad Awais, Manijeh Bashar, Josef Kittler:
How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation. 141-151 - Shunta Akiyama, Taiji Suzuki:
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. 152-162 - Maxwell Mbabilla Aladago, Lorenzo Torresani:
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks. 163-174 - Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell I. Nye, Armando Solar-Lezama
, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Joshua B. Tenenbaum:
A large-scale benchmark for few-shot program induction and synthesis. 175-186 - Ayya Alieva, Ashok Cutkosky, Abhimanyu Das:
Robust Pure Exploration in Linear Bandits with Limited Budget. 187-195 - Foivos Alimisis, Peter Davies, Dan Alistarh:
Communication-Efficient Distributed Optimization with Quantized Preconditioners. 196-206 - Pierre Alquier:
Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions. 207-218 - David Alvarez-Melis, Nicolò Fusi:
Dataset Dynamics via Gradient Flows in Probability Space. 219-230 - Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti-Spaccamela, Rebecca Reiffenhäuser:
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity. 231-242 - Sanae Amani, Christos Thrampoulidis, Lin Yang:
Safe Reinforcement Learning with Linear Function Approximation. 243-253 - Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven:
Automatic variational inference with cascading flows. 254-263 - Sebastian E. Ament, Carla P. Gomes:
Sparse Bayesian Learning via Stepwise Regression. 264-274 - Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup:
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards. 275-285 - Nishanth V. Anand, Doina Precup:
Preferential Temporal Difference Learning. 286-296 - Fidel Ernesto Diaz Andino, Maria Kokkou, Mateus de Oliveira Oliveira, Farhad Vadiee:
Unitary Branching Programs: Learnability and Lower Bounds. 297-306 - Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan A. DeCastro, Micah J. Fry, Daniela Rus:
The Logical Options Framework. 307-317 - Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. 318-330 - David Arbour, Drew Dimmery, Arjun Sondhi:
Permutation Weighting. 331-341 - Ludovic Arnould, Claire Boyer, Erwan Scornet:
Analyzing the tree-layer structure of Deep Forests. 342-350 - Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro:
Dropout: Explicit Forms and Capacity Control. 351-361 - Artem Artemev, David R. Burt, Mark van der Wilk:
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. 362-372 - Dilip Arumugam, Benjamin Van Roy:
Deciding What to Learn: A Rate-Distortion Approach. 373-382 - Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar:
Private Adaptive Gradient Methods for Convex Optimization. 383-392 - Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. 393-403 - Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai:
Combinatorial Blocking Bandits with Stochastic Delays. 404-413 - Julien Audiffren:
Dichotomous Optimistic Search to Quantify Human Perception. 414-424 - Dmitrii Avdiukhin, Shiva Prasad Kasiviswanathan:
Federated Learning under Arbitrary Communication Patterns. 425-435 - Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy:
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. 436-445 - Kyriakos Axiotis, Adam Karczmarz, Anish Mukherjee, Piotr Sankowski, Adrian Vladu:
Decomposable Submodular Function Minimization via Maximum Flow. 446-456 - Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. 457-467 - Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry:
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. 468-477 - Zahra Babaiee, Ramin M. Hasani, Mathias Lechner, Daniela Rus, Radu Grosu:
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification. 478-489 - Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann:
Uniform Convergence, Adversarial Spheres and a Simple Remedy. 490-499 - Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner:
Faster Kernel Matrix Algebra via Density Estimation. 500-510 - Kishan Panaganti Badrinath, Dileep Kalathil:
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees. 511-520 - Akhil Bagaria, Jason K. Senthil, George Konidaris:
Skill Discovery for Exploration and Planning using Deep Skill Graphs. 521-531 - Dara Bahri, Heinrich Jiang:
Locally Adaptive Label Smoothing Improves Predictive Churn. 532-542 - Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? 543-553 - Shaojie Bai, Vladlen Koltun, J. Zico Kolter:
Stabilizing Equilibrium Models by Jacobian Regularization. 554-565 - Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. 566-576 - Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. 577-587 - Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang:
GLSearch: Maximum Common Subgraph Detection via Learning to Search. 588-598 - Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Pascal Vasseur, Sébastien Adam, Paul Honeine:
Breaking the Limits of Message Passing Graph Neural Networks. 599-608 - Eric Balkanski, Sharon Qian, Yaron Singer:
Instance Specific Approximations for Submodular Maximization. 609-618 - Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen J. Roberts:
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment. 619-629 - Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Regularized Online Allocation Problems: Fairness and Beyond. 630-639 - Yujia Bao, Shiyu Chang, Regina Barzilay:
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers. 640-650 - Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang:
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. 651-661 - Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson:
Compositional Video Synthesis with Action Graphs. 662-673 - Nadav Barak, Sivan Sabato:
Approximating a Distribution Using Weight Queries. 674-683 - Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath:
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization. 684-693 - Burak Bartan, Mert Pilanci:
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming. 694-704 - Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman:
Beyond log2(T) regret for decentralized bandits in matching markets. 705-715 - Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard:
Optimal Thompson Sampling strategies for support-aware CVaR bandits. 716-726 - Dorian Baudry, Yoan Russac, Olivier Cappé:
On Limited-Memory Subsampling Strategies for Bandits. 727-737 - Matthias Bauer, Andriy Mnih:
Generalized Doubly Reparameterized Gradient Estimators. 738-747 - Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Lió:
Directional Graph Networks. 748-758 - Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. 759-768 - Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson:
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. 769-779 - Berkay Berabi, Jingxuan He, Veselin Raychev, Martin T. Vechev:
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer. 780-791 - Jeroen Berrevoets, Ahmed M. Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar:
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis. 792-802 - Patrice Bertail, Stéphan Clémençon, Yannick Guyonvarch, Nathan Noiry:
Learning from Biased Data: A Semi-Parametric Approach. 803-812 - Gedas Bertasius, Heng Wang, Lorenzo Torresani:
Is Space-Time Attention All You Need for Video Understanding? 813-824 - Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. 825-836 - Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro:
Size-Invariant Graph Representations for Graph Classification Extrapolations. 837-851 - Sourbh Bhadane, Aaron B. Wagner, Jayadev Acharya:
Principal Bit Analysis: Autoencoding with Schur-Concave Loss. 852-862 - Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal:
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. 863-873 - Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena:
Additive Error Guarantees for Weighted Low Rank Approximation. 874-883 - Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri:
Sample Complexity of Robust Linear Classification on Separated Data. 884-893 - Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar:
Finding k in Latent k- polytope. 894-903 - Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo:
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. 904-913 - André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. 914-924 - Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michal Misiurewicz, Georgios Piliouras:
Follow-the-Regularized-Leader Routes to Chaos in Routing Games. 925-935 - Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that scales. 936-945 - Max Biggs, Wei Sun, Markus Ettl:
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. 946-956 - Marin Bilos, Stephan Günnemann:
Scalable Normalizing Flows for Permutation Invariant Densities. 957-967 - Ilai Bistritz, Nicholas Bambos:
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games. 968-979 - Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger:
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision. 980-991 - Davis W. Blalock, John V. Guttag:
Multiplying Matrices Without Multiplying. 992-1004 - Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao:
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. 1005-1014 - Erik Bodin, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek:
Black-box density function estimation using recursive partitioning. 1015-1025 - Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein:
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. 1026-1037 - Roberto Bondesan, Max Welling:
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning. 1038-1048 - David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline Contextual Bandits with Overparameterized Models. 1049-1058 - Andy Brock, Soham De, Samuel L. Smith, Karen Simonyan:
High-Performance Large-Scale Image Recognition Without Normalization. 1059-1071 - James A. Brofos, Roy R. Lederman:
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo. 1072-1081 - Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh:
Reinforcement Learning of Implicit and Explicit Control Flow Instructions. 1082-1091 - Jonathan Brophy, Daniel Lowd:
Machine Unlearning for Random Forests. 1092-1104 - Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum:
Value Alignment Verification. 1105-1115 - David Bruns-Smith:
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain. 1116-1126 - Francois Buet-Golfouse:
Narrow Margins: Classification, Margins and Fat Tails. 1127-1135 - Mark Bun, Marek Eliás, Janardhan Kulkarni:
Differentially Private Correlation Clustering. 1136-1146 - Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi:
Disambiguation of Weak Supervision leading to Exponential Convergence rates. 1147-1157 - Diana Cai, Trevor Campbell, Tamara Broderick:
Finite mixture models do not reliably learn the number of components. 1158-1169 - Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei:
A Theory of Label Propagation for Subpopulation Shift. 1170-1182 - Xu Cai, Selwyn Gomes, Jonathan Scarlett:
Lenient Regret and Good-Action Identification in Gaussian Process Bandits. 1183-1192 - HanQin Cai, Yuchen Lou, Daniel McKenzie, Wotao Yin:
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization. 1193-1203 - Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang:
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training. 1204-1215 - Xu Cai, Jonathan Scarlett:
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization. 1216-1226 - Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels:
High-dimensional Experimental Design and Kernel Bandits. 1227-1237 - Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang:
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. 1238-1248 - Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli:
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. 1249-1260 - Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen:
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design. 1261-1271 - Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. 1272-1282 - Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta:
Parameter-free Locally Accelerated Conditional Gradients. 1283-1293 - Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda:
Optimizing persistent homology based functions. 1294-1303 - Asaf B. Cassel, Tomer Koren:
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret. 1304-1313 - Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti:
Multi-Receiver Online Bayesian Persuasion. 1314-1323 - Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss-Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach:
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. 1324-1335 - Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King:
Disentangling syntax and semantics in the brain with deep networks. 1336-1348 - L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi:
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees. 1349-1361 - Leonardo Cella, Massimiliano Pontil, Claudio Gentile:
Best Model Identification: A Rested Bandit Formulation. 1362-1372 - Johan Samir Obando-Ceron, Pablo Samuel Castro:
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. 1373-1383 - Edoardo Cetin, Oya Çeliktutan:
Learning Routines for Effective Off-Policy Reinforcement Learning. 1384-1394 - Ciwan Ceylan, Salla Franzén, Florian T. Pokorny:
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks. 1395-1406 - Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi:
GRAND: Graph Neural Diffusion. 1407-1418 - Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré:
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections. 1419-1429 - Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev:
Goal-Conditioned Reinforcement Learning with Imagined Subgoals. 1430-1440 - Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Locally Private k-Means in One Round. 1441-1451 - Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. 1452-1462