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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 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 S. 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 - Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler, José M. Álvarez:
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. 1463-1472 - Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis:
DeepWalking Backwards: From Embeddings Back to Graphs. 1473-1483 - Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik:
Differentiable Spatial Planning using Transformers. 1484-1495 - Henry Charlesworth, Giovanni Montana:
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning. 1496-1506 - Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama:
Classification with Rejection Based on Cost-sensitive Classification. 1507-1517 - Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine:
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. 1518-1528 - Xu Chen:
Unified Robust Semi-Supervised Variational Autoencoder. 1529-1538 - Boyuan Chen, Pieter Abbeel, Deepak Pathak:
Unsupervised Learning of Visual 3D Keypoints for Control. 1539-1549 - Rui Chen, Sanjeeb Dash, Tian Gao:
Integer Programming for Causal Structure Learning in the Presence of Latent Variables. 1550-1560 - Yifang Chen, Simon S. Du, Kevin Jamieson:
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. 1561-1570 - Yongxin Chen, Jiaojiao Fan, Amirhossein Taghvaei:
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks. 1571-1581 - Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves:
Neural Feature Matching in Implicit 3D Representations. 1582-1593 - Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour:
Decentralized Riemannian Gradient Descent on the Stiefel Manifold. 1594-1605 - Chao Chen, Haoyu Geng, Nianzu Yang, Junchi Yan, Daiyue Xue, Jianping Yu, Xiaokang Yang:
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation. 1606-1616 - Mayee F. Chen, Karan Goel, Nimit Sharad Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Ré:
Mandoline: Model Evaluation under Distribution Shift. 1617-1629 - Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu:
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. 1630-1639 - Dian Chen, Hongxin Hu, Qian Wang, Yinli Li, Cong Wang, Chao Shen, Qi Li:
CARTL: Cooperative Adversarially-Robust Transfer Learning. 1640-1650 - Liyu Chen, Haipeng Luo:
Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case. 1651-1660 - Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. 1661-1672 - Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama:
Large-Margin Contrastive Learning with Distance Polarization Regularizer. 1673-1683 - Yuzhou Chen, Ignacio Segovia-Dominguez, Yulia R. Gel:
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting. 1684-1694 - Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang:
A Unified Lottery Ticket Hypothesis for Graph Neural Networks. 1695-1706 - Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang:
Network Inference and Influence Maximization from Samples. 1707-1716 - Renyi Chen, Molei Tao:
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps. 1717-1727 - Tyler Chen, Thomas Trogdon, Shashanka Ubaru:
Analysis of stochastic Lanczos quadrature for spectrum approximation. 1728-1739 - Tianrong Chen, Ziyi Wang, Ioannis Exarchos, Evangelos A. Theodorou:
Large-Scale Multi-Agent Deep FBSDEs. 1740-1748 - Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long:
Representation Subspace Distance for Domain Adaptation Regression. 1749-1759 - Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. 1760-1770 - Xiangyu Chen, Min Ye:
Cyclically Equivariant Neural Decoders for Cyclic Codes. 1771-1780 - Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z. Chen, Jian Wu:
A Receptor Skeleton for Capsule Neural Networks. 1781-1790 - Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin:
Accelerating Gossip SGD with Periodic Global Averaging. 1791-1802 - Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph Gonzalez:
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. 1803-1813 - Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao, Yaohui Cai, Zhiru Zhang, Zhuo Feng:
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation. 1814-1824 - Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey:
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation. 1825-1835 - Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi:
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning. 1836-1845 - James Cheshire, Pierre Ménard, Alexandra Carpentier:
Problem Dependent View on Structured Thresholding Bandit Problems. 1846-1854 - Yun Kuen Cheung, Georgios Piliouras:
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence. 1855-1865 - Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao:
Understanding and Mitigating Accuracy Disparity in Regression. 1866-1876 - Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. 1877-1887 - Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Light RUMs. 1888-1897 - Narsimha Reddy Chilkuri, Chris Eliasmith:
Parallelizing Legendre Memory Unit Training. 1898-1907 - Uthsav Chitra, Kimberly Ding, Jasper C. H. Lee, Benjamin J. Raphael:
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies. 1908-1919 - Jakub Chledowski, Adam Polak, Bartosz Szabucki, Konrad Tomasz Zolna:
Robust Learning-Augmented Caching: An Experimental Study. 1920-1930 - Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal:
Unifying Vision-and-Language Tasks via Text Generation. 1931-1942 - Youngwon Choi, Sungdong Lee, Joong-Ho Won:
Learning from Nested Data with Ornstein Auto-Encoders. 1943-1952 - Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu:
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning. 1953-1963 - Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. 1964-1974 - Jishnu Ray Chowdhury, Cornelia Caragea:
Modeling Hierarchical Structures with Continuous Recursive Neural Networks. 1975-1988 - Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht:
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing. 1989-1998 - Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux:
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. 1999-2009 - Julien Grand-Clément, Christian Kroer:
First-Order Methods for Wasserstein Distributionally Robust MDP. 2010-2019 - Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman:
Phasic Policy Gradient. 2020-2027 - Samuel Cohen, Brandon Amos, Yaron Lipman:
Riemannian Convex Potential Maps. 2028-2038 - Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu:
Scaling Properties of Deep Residual Networks. 2039-2048 - Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Differentially-Private Clustering of Easy Instances. 2049-2059 - Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde:
Improving Ultrametrics Embeddings Through Coresets. 2060-2068 - Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski:
Correlation Clustering in Constant Many Parallel Rounds. 2069-2078 - Fabien Collas, Ekhine Irurozki:
Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability. 2079-2088 - Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai:
Exploiting Shared Representations for Personalized Federated Learning. 2089-2099 - Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet:
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. 2100-2111 - José Correa, Andrés Cristi, Paul Duetting, Ashkan Norouzi-Fard:
Fairness and Bias in Online Selection. 2112-2121 - Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh:
Relative Deviation Margin Bounds. 2122-2131 - Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang:
A Discriminative Technique for Multiple-Source Adaptation. 2132-2143 - Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova:
Characterizing Fairness Over the Set of Good Models Under Selective Labels. 2144-2155 - Romain Couillet, Florent Chatelain, Nicolas Le Bihan:
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering. 2156-2165 - Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. 2166-2177 - Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. 2178-2188 - Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel:
Environment Inference for Invariant Learning. 2189-2200 - Francesco Croce, Matthias Hein:
Mind the Box: l1-APGD for Sparse Adversarial Attacks on Image Classifiers. 2201-2211 - Wentao Cui, Yuhong Guo:
Parameterless Transductive Feature Re-representation for Few-Shot Learning. 2212-2221 - Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu, He Huang:
Randomized Algorithms for Submodular Function Maximization with a k-System Constraint. 2222-2232 - Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin:
GBHT: Gradient Boosting Histogram Transform for Density Estimation. 2233-2243 - Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O'Boyle, Hugh Leather:
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. 2244-2253 - Sebastian Curi, Ilija Bogunovic, Andreas Krause:
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning. 2254-2264 - Mihaela Curmei, Sarah Dean, Benjamin Recht:
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability. 2265-2275 - Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit:
Dynamic Balancing for Model Selection in Bandits and RL. 2276-2285 - Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun:
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. 2286-2296 - Tommaso d'Orsi, Gleb Novikov, David Steurer:
Consistent regression when oblivious outliers overwhelm. 2297-2306 - Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. 2307-2318 - Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava:
A Tale of Two Efficient and Informative Negative Sampling Distributions. 2319-2329 - Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao, Amit Singh, Sumeet Agarwal, Purushottam Kar, Manik Varma:
SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels. 2330-2340 - Yogesh Dahiya, Fedor V. Fomin, Fahad Panolan, Kirill Simonov:
Fixed-Parameter and Approximation Algorithms for PCA with Outliers. 2341-2351 - Biwei Dai, Uros Seljak:
Sliced Iterative Normalizing Flows. 2352-2364 - Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. 2365-2375 - Christopher R. Dance, Julien Perez, Théo Cachet:
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation. 2376-2387 - Mohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram:
Re-understanding Finite-State Representations of Recurrent Policy Networks. 2388-2397 - Amir Daneshmand, Gesualdo Scutari, Pavel E. Dvurechensky, Alexander V. Gasnikov:
Newton Method over Networks is Fast up to the Statistical Precision. 2398-2409 - Dominic Danks, Christopher Yau:
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders. 2410-2420 - Giannis Daras, Joseph Dean, Ajil Jalal, Alex Dimakis:
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models. 2421-2432 - Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel:
Measuring Robustness in Deep Learning Based Compressive Sensing. 2433-2444 - Lokesh Chandra Das, Myounggyu Won:
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning. 2445-2455 - George Dasoulas, Kevin Scaman, Aladin Virmaux:
Lipschitz normalization for self-attention layers with application to graph neural networks. 2456-2466 - Jyotikrishna Dass, Rabi N. Mahapatra:
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers. 2467-2477 - Deepesh Data, Suhas N. Diggavi:
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. 2478-2488 - Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Ré, Chelsea Finn, Percy Liang:
Catformer: Designing Stable Transformers via Sensitivity Analysis. 2489-2499 - Quinlan Dawkins, Tianxi Li, Haifeng Xu:
Diffusion Source Identification on Networks with Statistical Confidence. 2500-2509 - Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Bayesian Deep Learning via Subnetwork Inference. 2510-2521 - Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd:
Adversarial Robustness Guarantees for Random Deep Neural Networks. 2522-2534 - Filip de Roos, Alexandra Gessner, Philipp Hennig:
High-Dimensional Gaussian Process Inference with Derivatives. 2535-2545 - Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen:
Transfer-Based Semantic Anomaly Detection. 2546-2558 - Javier Dehesa, Andrew Vidler, Julian A. Padget, Christof Lutteroth:
Grid-Functioned Neural Networks. 2559-2567 - Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel:
Multidimensional Scaling: Approximation and Complexity. 2568-2578 - Weijian Deng, Stephen Gould, Liang Zheng:
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? 2579-2589 - Zhun Deng, Hangfeng He, Weijie J. Su:
Toward Better Generalization Bounds with Locally Elastic Stability. 2590-2600 - Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni, Song Zuo:
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing. 2601-2610 - Don Kurian Dennis, Tian Li, Virginia Smith:
Heterogeneity for the Win: One-Shot Federated Clustering. 2611-2620 - Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees Snoek:
Kernel Continual Learning. 2621-2631 - Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa:
Bayesian Optimization over Hybrid Spaces. 2632-2643 - Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann:
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. 2644-2653 - Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. 2654-2664 - Oussama Dhifallah, Yue M. Lu:
On the Inherent Regularization Effects of Noise Injection During Training. 2665-2675 - Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time. 2676-2686 - Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis:
Learning Online Algorithms with Distributional Advice. 2687-2696 - Theo Diamandis, Yonina C. Eldar, Alireza Fallah, Farzan Farnia, Asuman E. Ozdaglar:
A Wasserstein Minimax Framework for Mixed Linear Regression. 2697-2706 - Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor:
Context-Aware Online Collective Inference for Templated Graphical Models. 2707-2716 - Aleksandar Dimitriev, Mingyuan Zhou:
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables. 2717-2727 - Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue:
XOR-CD: Linearly Convergent Constrained Structure Generation. 2728-2738 - Tianyu Ding, Zhihui Zhu, René Vidal, Daniel P. Robinson:
Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach. 2739-2748 - Tuan Dinh, Kangwook Lee:
Coded-InvNet for Resilient Prediction Serving Systems. 2749-2759 - Vincent Divol, Théo Lacombe:
Estimation and Quantization of Expected Persistence Diagrams. 2760-2770 - Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. 2771-2782 - Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko:
Kernel-Based Reinforcement Learning: A Finite-Time Analysis. 2783-2792 - Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas:
Attention is not all you need: pure attention loses rank doubly exponentially with depth. 2793-2803 - Konstantin Donhauser, Mingqi Wu, Fanny Yang:
How rotational invariance of common kernels prevents generalization in high dimensions. 2804-2814 - Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx:
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. 2815-2825 - Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang:
Bilinear Classes: A Structural Framework for Provable Generalization in RL. 2826-2836 - Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch:
Improved Contrastive Divergence Training of Energy-Based Models. 2837-2848 - Cunxiao Du, Zhaopeng Tu, Jing Jiang:
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation. 2849-2859 - Elbert Du, Franklyn Wang, Michael Mitzenmacher:
Putting the "Learning" into Learning-Augmented Algorithms for Frequency Estimation. 2860-2869 - Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang:
Estimating α-Rank from A Few Entries with Low Rank Matrix Completion. 2870-2879 - Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. 2880-2891 - Yaqi Duan, Chi Jin, Zhiyuan Li:
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning. 2892-2902 - Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou:
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network. 2903-2913 - Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra:
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics. 2914-2925 - Adrien Ecoffet, Joel Lehman:
Reinforcement Learning Under Moral Uncertainty. 2926-2936 - Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor:
Confidence-Budget Matching for Sequential Budgeted Learning. 2937-2947 - Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. 2948-2958 - Bryn Elesedy, Sheheryar Zaidi:
Provably Strict Generalisation Benefit for Equivariant Models. 2959-2969 - Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan:
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations. 2970-2981 - Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher:
Implicit Bias of Linear RNNs. 2982-2992 - Tolga Ergen, Mert Pilanci:
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs. 2993-3003 - Tolga Ergen, Mert Pilanci:
Revealing the Structure of Deep Neural Networks via Convex Duality. 3004-3014 - Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe:
Whitening for Self-Supervised Representation Learning. 3015-3024 - Federico Errica, Davide Bacciu, Alessio Micheli:
Graph Mixture Density Networks. 3025-3035 - Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar:
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data. 3036-3046 - Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy M. Hospedales:
Weight-covariance alignment for adversarially robust neural networks. 3047-3056 - Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi:
Data augmentation for deep learning based accelerated MRI reconstruction with limited data. 3057-3067 - Xuhui Fan, Bin Li, Yaqiong Li, Scott A. Sisson:
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks. 3068-3077 - Ying Fan, Yifei Ming:
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling. 3078-3087 - Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Animashree Anandkumar:
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies. 3088-3099 - Guanhua Fang, Ping Li:
On Estimation in Latent Variable Models. 3100-3110 - Guanhua Fang, Ping Li:
On Variational Inference in Biclustering Models. 3111-3121 - Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. 3122-3132 - Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe:
Streaming Bayesian Deep Tensor Factorization. 3133-3142 - Amir Massoud Farahmand, Mohammad Ghavamzadeh:
PID Accelerated Value Iteration Algorithm. 3143-3153 - Vivek F. Farias, Andrew A. Li, Tianyi Peng:
Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise. 3154-3163 - Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm:
Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results. 3164-3173 - Farzan Farnia, Asuman E. Ozdaglar:
Train simultaneously, generalize better: Stability of gradient-based minimax learners. 3174-3185 - Kilian Fatras, Thibault Séjourné, Rémi Flamary, Nicolas Courty:
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation. 3186-3197 - Yingjie Fei, Zhuoran Yang, Zhaoran Wang:
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach. 3198-3207 - Vitaly Feldman, Kunal Talwar:
Lossless Compression of Efficient Private Local Randomizers. 3208-3219 - Zhili Feng, Praneeth Kacham, David P. Woodruff:
Dimensionality Reduction for the Sum-of-Distances Metric. 3220-3229 - Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye:
Reserve Price Optimization for First Price Auctions in Display Advertising. 3230-3239 - Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha:
Uncertainty Principles of Encoding GANs. 3240-3251 - Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. 3252-3262 - Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang:
Provably Correct Optimization and Exploration with Non-linear Policies. 3263-3273 - Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, Wei Chen:
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. 3274-3283 - Ruili Feng, Deli Zhao, Zheng-Jun Zha:
Understanding Noise Injection in GANs. 3284-3293 - Matthias Fey, Jan Eric Lenssen, Frank Weichert, Jure Leskovec:
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings. 3294-3304 - Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. 3305-3317 - Marc Finzi, Max Welling, Andrew Gordon Wilson:
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. 3318-3328 - Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay:
Few-Shot Conformal Prediction with Auxiliary Tasks. 3329-3339 - Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. 3340-3351 - Jonas Fischer, Anna Oláh, Jilles Vreeken:
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. 3352-3362 - Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey:
Online Learning with Optimism and Delay. 3363-3373 - Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet:
Online A-Optimal Design and Active Linear Regression. 3374-3383 - Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth:
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. 3384-3395 - Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis:
Efficient Online Learning for Dynamic k-Clustering. 3396-3406 - Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. 3407-3416 - Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. 3417-3426 - Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. 3427-3438 - Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada:
Post-selection inference with HSIC-Lasso. 3439-3448 - Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer:
Variational Data Assimilation with a Learned Inverse Observation Operator. 3449-3458 - Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis:
Bayesian Quadrature on Riemannian Data Manifolds. 3459-3468 - Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao:
Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing. 3469-3479 - Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. 3480-3491 - Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin:
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference. 3492-3504 - Yonggan Fu, Yongan Zhang, Yang Zhang, David D. Cox, Yingyan Lin:
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. 3505-3517 - Scott Fujimoto, David Meger, Doina Precup:
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. 3518-3529 - Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodolà:
Learning disentangled representations via product manifold projection. 3530-3540 - Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu:
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. 3541-3552 - Yansong Gao, Pratik Chaudhari:
An Information-Geometric Distance on the Space of Tasks. 3553-3563 - Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. 3564-3575 - Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen:
Unsupervised Co-part Segmentation through Assembly. 3576-3586 - Yi Gao, Min-Ling Zhang:
Discriminative Complementary-Label Learning with Weighted Loss. 3587-3597 - Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton:
RATT: Leveraging Unlabeled Data to Guarantee Generalization. 3598-3609 - Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar:
On Proximal Policy Optimization's Heavy-tailed Gradients. 3610-3619 - Damien Garreau, Dina Mardaoui:
What does LIME really see in images? 3620-3629 - Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont, Boammani Aser Lompo:
Parametric Graph for Unimodal Ranking Bandit. 3630-3639 - Floris Geerts, Filip Mazowiecki, Guillermo A. Pérez:
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework. 3640-3649 - Tomas Geffner, Justin Domke:
On the difficulty of unbiased alpha divergence minimization. 3650-3659 - Amanda Gentzel, Purva Pruthi, David D. Jensen:
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference. 3660-3671 - Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld:
Strategic Classification in the Dark. 3672-3681 - Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu:
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. 3682-3691 - Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha:
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. 3692-3701 - Rohan Ghuge, Anupam Gupta, Viswanath Nagarajan:
The Power of Adaptivity for Stochastic Submodular Cover. 3702-3712 - Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. 3713-3722 - Grzegorz Gluch, Rüdiger L. Urbanke:
Query Complexity of Adversarial Attacks. 3723-3733 - Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu:
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective. 3734-3744 - Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha:
12-Lead ECG Reconstruction via Koopman Operators. 3745-3754 - Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf:
Function Contrastive Learning of Transferable Meta-Representations. 3755-3765 - Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato:
Active Slices for Sliced Stein Discrepancy. 3766-3776 - Sruthi Gorantla, Amit Deshpande, Anand Louis:
On the Problem of Underranking in Group-Fair Ranking. 3777-3787 - Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
MARINA: Faster Non-Convex Distributed Learning with Compression. 3788-3798 - Martijn Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova:
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures. 3799-3808 - Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng:
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline. 3809-3820 - Florian Graf, Christoph D. Hofer, Marc Niethammer, Roland Kwitt:
Dissecting Supervised Constrastive Learning. 3821-3830