| 2021 | "Hey, that's not an ODE": Faster ODE Adjoints via Seminorms. Patrick Kidger, Ricky T. Q. Chen, Terry J. Lyons |
| 2021 | 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He |
| 2021 | 12-Lead ECG Reconstruction via Koopman Operators. Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha |
| 2021 | A Bit More Bayesian: Domain-Invariant Learning with Uncertainty. Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao, Cees Snoek |
| 2021 | A Collective Learning Framework to Boost GNN Expressiveness for Node Classification. Mengyue Hang, Jennifer Neville, Bruno Ribeiro |
| 2021 | A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. Scott Fujimoto, David Meger, Doina Precup |
| 2021 | A Differentiable Point Process with Its Application to Spiking Neural Networks. Hiroshi Kajino |
| 2021 | A Discriminative Technique for Multiple-Source Adaptation. Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang |
| 2021 | A Distribution-dependent Analysis of Meta Learning. Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvári |
| 2021 | A Framework for Private Matrix Analysis in Sliding Window Model. Jalaj Upadhyay, Sarvagya Upadhyay |
| 2021 | A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration. Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu |
| 2021 | A Functional Perspective on Learning Symmetric Functions with Neural Networks. Aaron Zweig, Joan Bruna |
| 2021 | A General Framework For Detecting Anomalous Inputs to DNN Classifiers. Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee |
| 2021 | A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang |
| 2021 | A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization. Ran Xin, Usman A. Khan, Soummya Kar |
| 2021 | A Language for Counterfactual Generative Models. Zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama |
| 2021 | A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. Abi Komanduru, Jean Honorio |
| 2021 | A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network. Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy |
| 2021 | A New Formalism, Method and Open Issues for Zero-Shot Coordination. Johannes Treutlein, Michael Dennis, Caspar Oesterheld, Jakob N. Foerster |
| 2021 | A New Representation of Successor Features for Transfer across Dissimilar Environments. Majid Abdolshah, Hung Le, Thommen George Karimpanal, Sunil Gupta, Santu Rana, Svetha Venkatesh |
| 2021 | A Novel Method to Solve Neural Knapsack Problems. Duanshun Li, Jing Liu, Dongeun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, Noseong Park |
| 2021 | A Novel Sequential Coreset Method for Gradient Descent Algorithms. Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos M. Freris, Hu Ding |
| 2021 | A Nullspace Property for Subspace-Preserving Recovery. Mustafa Devrim Kaba, Chong You, Daniel P. Robinson, Enrique Mallada, René Vidal |
| 2021 | A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How |
| 2021 | A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. Marc Finzi, Max Welling, Andrew Gordon Wilson |
| 2021 | A Precise Performance Analysis of Support Vector Regression. Houssem Sifaou, Abla Kammoun, Mohamed-Slim Alouini |
| 2021 | A Probabilistic Approach to Neural Network Pruning. Xin Qian, Diego Klabjan |
| 2021 | A Proxy Variable View of Shared Confounding. Yixin Wang, David M. Blei |
| 2021 | A Receptor Skeleton for Capsule Neural Networks. Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z. Chen, Jian Wu |
| 2021 | A Regret Minimization Approach to Iterative Learning Control. Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh |
| 2021 | A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning. Nikunj Saunshi, Arushi Gupta, Wei Hu |
| 2021 | A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance. Minhui Huang, Shiqian Ma, Lifeng Lai |
| 2021 | A Sampling-Based Method for Tensor Ring Decomposition. Osman Asif Malik, Stephen Becker |
| 2021 | A Scalable Deterministic Global Optimization Algorithm for Clustering Problems. Kaixun Hua, Mingfei Shi, Yankai Cao |
| 2021 | A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples. Christian Kümmerle, Claudio Mayrink Verdun |
| 2021 | A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance. Xiaoyu Li, Zhenxun Zhuang, Francesco Orabona |
| 2021 | A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin |
| 2021 | A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting. Eli N. Weinstein, Debora S. Marks |
| 2021 | A Tale of Two Efficient and Informative Negative Sampling Distributions. Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen, Mengnan Zhao, Anshumali Shrivastava |
| 2021 | A Theory of Label Propagation for Subpopulation Shift. Tianle Cai, Ruiqi Gao, Jason D. Lee, Qi Lei |
| 2021 | A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention. Tomoki Watanabe, Paolo Favaro |
| 2021 | A Unified Lottery Ticket Hypothesis for Graph Neural Networks. Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang |
| 2021 | A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization. Risheng Liu, Xuan Liu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang |
| 2021 | A Wasserstein Minimax Framework for Mixed Linear Regression. Theo Diamandis, Yonina C. Eldar, Alireza Fallah, Farzan Farnia, Asuman E. Ozdaglar |
| 2021 | A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization. HanQin Cai, Yuchen Lou, Daniel McKenzie, Wotao Yin |
| 2021 | A large-scale benchmark for few-shot program induction and synthesis. Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell I. Nye, Armando Solar-Lezama, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Joshua B. Tenenbaum |
| 2021 | A statistical perspective on distillation. Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar |
| 2021 | A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions. Gabriel Mel, Surya Ganguli |
| 2021 | ACE: Explaining cluster from an adversarial perspective. Yang Young Lu, Timothy C. Yu, Giancarlo Bonora, William Stafford Noble |
| 2021 | ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov |
| 2021 | AGENT: A Benchmark for Core Psychological Reasoning. Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith, Shari Liu, Dan Gutfreund, Elizabeth S. Spelke, Joshua B. Tenenbaum, Tomer D. Ullman |
| 2021 | APS: Active Pretraining with Successor Features. Hao Liu, Pieter Abbeel |
| 2021 | ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables. Aleksandar Dimitriev, Mingyuan Zhou |
| 2021 | ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks. Jungmin Kwon, Jeongseop Kim, Hyunseo Park, In Kwon Choi |
| 2021 | Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework. Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu |
| 2021 | Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm. Taeho Yoon, Ernest K. Ryu |
| 2021 | Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon |
| 2021 | Accelerating Gossip SGD with Periodic Global Averaging. Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin |
| 2021 | Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies. Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge |
| 2021 | Acceleration via Fractal Learning Rate Schedules. Naman Agarwal, Surbhi Goel, Cyril Zhang |
| 2021 | Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks. Huiping Zhuang, Zhenyu Weng, Fulin Luo, Kar-Ann Toj, Haizhou Li, Zhiping Lin |
| 2021 | Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt |
| 2021 | Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni |
| 2021 | Accurate Post Training Quantization With Small Calibration Sets. Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry |
| 2021 | Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously. Chung-wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang, Xiaojin Zhang |
| 2021 | ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. Jianfei Chen, Lianmin Zheng, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, Joseph Gonzalez |
| 2021 | Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine |
| 2021 | Active Covering. Heinrich Jiang, Afshin Rostamizadeh |
| 2021 | Active Deep Probabilistic Subsampling. Hans Van Gorp, Iris A. M. Huijben, Bastiaan S. Veeling, Nicola Pezzotti, Ruud J. G. van Sloun |
| 2021 | Active Feature Acquisition with Generative Surrogate Models. Yang Li, Junier Oliva |
| 2021 | Active Learning for Distributionally Robust Level-Set Estimation. Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi |
| 2021 | Active Learning of Continuous-time Bayesian Networks through Interventions. Dominik Linzner, Heinz Koeppl |
| 2021 | Active Slices for Sliced Stein Discrepancy. Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato |
| 2021 | Active Testing: Sample-Efficient Model Evaluation. Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth |
| 2021 | AdaXpert: Adapting Neural Architecture for Growing Data. Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo, Peilin Zhao, Peng Wang, Mingkui Tan |
| 2021 | Adapting to Delays and Data in Adversarial Multi-Armed Bandits. András György, Pooria Joulani |
| 2021 | Adapting to misspecification in contextual bandits with offline regression oracles. Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey |
| 2021 | Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality. Jonathan Lacotte, Yifei Wang, Mert Pilanci |
| 2021 | Adaptive Sampling for Best Policy Identification in Markov Decision Processes. Aymen Al Marjani, Alexandre Proutière |
| 2021 | Additive Error Guarantees for Weighted Low Rank Approximation. Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana, Maheshakya Wijewardena |
| 2021 | Addressing Catastrophic Forgetting in Few-Shot Problems. Pau Ching Yap, Hippolyt Ritter, David Barber |
| 2021 | Adversarial Combinatorial Bandits with General Non-linear Reward Functions. Yanjun Han, Yining Wang, Xi Chen |
| 2021 | Adversarial Dueling Bandits. Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2021 | Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H. Bach, Eli Upfal |
| 2021 | Adversarial Option-Aware Hierarchical Imitation Learning. Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei Li |
| 2021 | Adversarial Policy Learning in Two-player Competitive Games. Wenbo Guo, Xian Wu, Sui Huang, Xinyu Xing |
| 2021 | Adversarial Purification with Score-based Generative Models. Jongmin Yoon, Sung Ju Hwang, Juho Lee |
| 2021 | Adversarial Robustness Guarantees for Random Deep Neural Networks. Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd |
| 2021 | Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets. Thomas Kerdreux, Lewis Liu, Simon Lacoste-Julien, Damien Scieur |
| 2021 | Aggregating From Multiple Target-Shifted Sources. Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles X. Ling, Boyu Wang |
| 2021 | Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. Spencer Frei, Yuan Cao, Quanquan Gu |
| 2021 | Align, then memorise: the dynamics of learning with feedback alignment. Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt |
| 2021 | Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu |
| 2021 | AlphaNet: Improved Training of Supernets with Alpha-Divergence. Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra |
| 2021 | Alternative Microfoundations for Strategic Classification. Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt |
| 2021 | Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. Aurick Zhou, Sergey Levine |
| 2021 | An Algorithm for Stochastic and Adversarial Bandits with Switching Costs. Chloé Rouyer, Yevgeny Seldin, Nicolò Cesa-Bianchi |
| 2021 | An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang |
| 2021 | An Identifiable Double VAE For Disentangled Representations. Graziano Mita, Maurizio Filippone, Pietro Michiardi |
| 2021 | An Information-Geometric Distance on the Space of Tasks. Yansong Gao, Pratik Chaudhari |
| 2021 | An Integer Linear Programming Framework for Mining Constraints from Data. Tao Meng, Kai-Wei Chang |
| 2021 | An exact solver for the Weston-Watkins SVM subproblem. Yutong Wang, Clayton Scott |
| 2021 | Analysis of stochastic Lanczos quadrature for spectrum approximation. Tyler Chen, Thomas Trogdon, Shashanka Ubaru |
| 2021 | Analyzing the tree-layer structure of Deep Forests. Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2021 | Annealed Flow Transport Monte Carlo. Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet |
| 2021 | Approximate Group Fairness for Clustering. Bo Li, Lijun Li, Ankang Sun, Chenhao Wang, Yingfan Wang |
| 2021 | Approximating a Distribution Using Weight Queries. Nadav Barak, Sivan Sabato |
| 2021 | Approximation Theory Based Methods for RKHS Bandits. Sho Takemori, Masahiro Sato |
| 2021 | Approximation Theory of Convolutional Architectures for Time Series Modelling. Haotian Jiang, Zhong Li, Qianxiao Li |
| 2021 | Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Gürbüzbalaban, Umut Simsekli |
| 2021 | Asymmetric Loss Functions for Learning with Noisy Labels. Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji |
| 2021 | Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator. Zeng Li, Chuanlong Xie, Qinwen Wang |
| 2021 | Asymptotics of Ridge Regression in Convolutional Models. Mojtaba Sahraee-Ardakan, Tung Mai, Anup B. Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K. Fletcher |
| 2021 | Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction. Kenta Niwa, Guoqiang Zhang, W. Bastiaan Kleijn, Noboru Harada, Hiroshi Sawada, Akinori Fujino |
| 2021 | Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy |
| 2021 | Attention is not all you need: pure attention loses rank doubly exponentially with depth. Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas |
| 2021 | Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment. Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen J. Roberts |
| 2021 | Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. Yonggan Fu, Yongan Zhang, Yang Zhang, David D. Cox, Yingyan Lin |
| 2021 | AutoAttend: Automated Attention Representation Search. Chaoyu Guan, Xin Wang, Wenwu Zhu |
| 2021 | AutoSampling: Search for Effective Data Sampling Schedules. Ming Sun, Haoxuan Dou, Baopu Li, Junjie Yan, Wanli Ouyang, Lei Cui |
| 2021 | Autoencoder Image Interpolation by Shaping the Latent Space. Alon Oring, Zohar Yakhini, Yacov Hel-Or |
| 2021 | Autoencoding Under Normalization Constraints. Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park |
| 2021 | Automatic variational inference with cascading flows. Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven |
| 2021 | Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf |
| 2021 | Average-Reward Off-Policy Policy Evaluation with Function Approximation. Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson |
| 2021 | BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining. Weizhen Qi, Yeyun Gong, Jian Jiao, Yu Yan, Weizhu Chen, Dayiheng Liu, Kewen Tang, Houqiang Li, Jiusheng Chen, Ruofei Zhang, Ming Zhou, Nan Duan |
| 2021 | BASE Layers: Simplifying Training of Large, Sparse Models. Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer |
| 2021 | BASGD: Buffered Asynchronous SGD for Byzantine Learning. Yi-Rui Yang, Wu-Jun Li |
| 2021 | BORE: Bayesian Optimization by Density-Ratio Estimation. Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos |
| 2021 | Backdoor Scanning for Deep Neural Networks through K-Arm Optimization. Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, Xiangyu Zhang |
| 2021 | Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks. Yukun Yang, Wenrui Zhang, Peng Li |
| 2021 | Barlow Twins: Self-Supervised Learning via Redundancy Reduction. Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny |
| 2021 | BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders. Dominic Danks, Christopher Yau |
| 2021 | Batch Value-function Approximation with Only Realizability. Tengyang Xie, Nan Jiang |
| 2021 | Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. Willie Neiswanger, Ke Alexander Wang, Stefano Ermon |
| 2021 | Bayesian Attention Belief Networks. Shujian Zhang, Xinjie Fan, Bo Chen, Mingyuan Zhou |
| 2021 | Bayesian Deep Learning via Subnetwork Inference. Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato |
| 2021 | Bayesian Optimistic Optimisation with Exponentially Decaying Regret. Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh |
| 2021 | Bayesian Optimization over Hybrid Spaces. Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa |
| 2021 | Bayesian Quadrature on Riemannian Data Manifolds. Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis |
| 2021 | Bayesian Structural Adaptation for Continual Learning. Abhishek Kumar, Sunabha Chatterjee, Piyush Rai |
| 2021 | Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. Andrew Slavin Ross, Finale Doshi-Velez |
| 2021 | Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao |
| 2021 | Best Arm Identification in Graphical Bilinear Bandits. Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre |
| 2021 | Best Model Identification: A Rested Bandit Formulation. Leonardo Cella, Massimiliano Pontil, Claudio Gentile |
| 2021 | Better Training using Weight-Constrained Stochastic Dynamics. Benedict J. Leimkuhler, Tiffany J. Vlaar, Timothée Pouchon, Amos J. Storkey |
| 2021 | Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization. Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux |
| 2021 | Beyond log Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman |
| 2021 | Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design. Gustavo Malkomes, Bolong Cheng, Eric Hans Lee, Mike Mccourt |
| 2021 | Bias-Free Scalable Gaussian Processes via Randomized Truncations. Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham |
| 2021 | Bias-Robust Bayesian Optimization via Dueling Bandits. Johannes Kirschner, Andreas Krause |
| 2021 | Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. Tomoya Murata, Taiji Suzuki |
| 2021 | Bilevel Optimization: Convergence Analysis and Enhanced Design. Kaiyi Ji, Junjie Yang, Yingbin Liang |
| 2021 | Bilinear Classes: A Structural Framework for Provable Generalization in RL. Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang |
| 2021 | Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2021 | Black-box density function estimation using recursive partitioning. Erik Bodin, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek |
| 2021 | Blind Pareto Fairness and Subgroup Robustness. Natalia Martínez, Martín Bertrán, Afroditi Papadaki, Miguel R. D. Rodrigues, Guillermo Sapiro |
| 2021 | Boosting for Online Convex Optimization. Elad Hazan, Karan Singh |
| 2021 | Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size. Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak |
| 2021 | Bootstrapping Fitted Q-Evaluation for Off-Policy Inference. Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang |
| 2021 | Break-It-Fix-It: Unsupervised Learning for Program Repair. Michihiro Yasunaga, Percy Liang |
| 2021 | Breaking the Deadly Triad with a Target Network. Shangtong Zhang, Hengshuai Yao, Shimon Whiteson |
| 2021 | Breaking the Limits of Message Passing Graph Neural Networks. Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Pascal Vasseur, Sébastien Adam, Paul Honeine |
| 2021 | Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation. Haoxiang Wang, Han Zhao, Bo Li |
| 2021 | Budgeted Heterogeneous Treatment Effect Estimation. Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou |
| 2021 | Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. Deepesh Data, Suhas N. Diggavi |
| 2021 | CARTL: Cooperative Adversarially-Robust Transfer Learning. Dian Chen, Hongxin Hu, Qian Wang, Yinli Li, Cong Wang, Chao Shen, Qi Li |
| 2021 | CATE: Computation-aware Neural Architecture Encoding with Transformers. Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang |
| 2021 | CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama |
| 2021 | CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients. Dani Kiyasseh, Tingting Zhu, David A. Clifton |
| 2021 | CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li |
| 2021 | CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. Tengyu Xu, Yingbin Liang, Guanghui Lan |
| 2021 | CURI: A Benchmark for Productive Concept Learning Under Uncertainty. Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake |
| 2021 | Calibrate Before Use: Improving Few-shot Performance of Language Models. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh |
| 2021 | Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville |
| 2021 | Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization. Stanislaw Jastrzebski, Devansh Arpit, Oliver Åstrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof J. Geras |
| 2021 | Catformer: Designing Stable Transformers via Sensitivity Analysis. Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Ré, Chelsea Finn, Percy Liang |
| 2021 | Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning. Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf |
| 2021 | Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners. Elias Chaibub Neto |
| 2021 | ChaCha for Online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro, Marco Rossi |
| 2021 | Characterizing Fairness Over the Set of Good Models Under Selective Labels. Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova |
| 2021 | Characterizing Structural Regularities of Labeled Data in Overparameterized Models. Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer |
| 2021 | Characterizing the Gap Between Actor-Critic and Policy Gradient. Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans |
| 2021 | Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication. Sangwoo Hong, Heecheol Yang, YoungSeok Yoon, Taehyun Cho, Jungwoo Lee |
| 2021 | Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu |
| 2021 | Classification with Rejection Based on Cost-sensitive Classification. Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama |
| 2021 | Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed. Maria Refinetti, Sebastian Goldt, Florent Krzakala, Lenka Zdeborová |
| 2021 | Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels. Zhaowei Zhu, Yiwen Song, Yang Liu |
| 2021 | Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi |
| 2021 | Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar |
| 2021 | Coded-InvNet for Resilient Prediction Serving Systems. Tuan Dinh, Kangwook Lee |
| 2021 | Collaborative Bayesian Optimization with Fair Regret. Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints. Anselm Paulus, Michal Rolínek, Vít Musil, Brandon Amos, Georg Martius |
| 2021 | Combinatorial Blocking Bandits with Stochastic Delays. Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai |
| 2021 | Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning. Sebastian Curi, Ilija Bogunovic, Andreas Krause |
| 2021 | Communication-Efficient Distributed Optimization with Quantized Preconditioners. Foivos Alimisis, Peter Davies, Dan Alistarh |
| 2021 | Communication-Efficient Distributed SVD via Local Power Iterations. Xiang Li, Shusen Wang, Kun Chen, Zhihua Zhang |
| 2021 | Commutative Lie Group VAE for Disentanglement Learning. Xinqi Zhu, Chang Xu, Dacheng Tao |
| 2021 | Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization. Sang Michael Xie, Tengyu Ma, Percy Liang |
| 2021 | Composing Normalizing Flows for Inverse Problems. Jay Whang, Erik M. Lindgren, Alex Dimakis |
| 2021 | Compositional Video Synthesis with Action Graphs. Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson |
| 2021 | Compressed Maximum Likelihood. Yi Hao, Alon Orlitsky |
| 2021 | ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun |
| 2021 | Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability. Fabien Collas, Ekhine Irurozki |
| 2021 | Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet |
| 2021 | Conditional Temporal Neural Processes with Covariance Loss. Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi |
| 2021 | Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Jaehyeon Kim, Jungil Kong, Juhee Son |
| 2021 | Confidence Scores Make Instance-dependent Label-noise Learning Possible. Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama |
| 2021 | Confidence-Budget Matching for Sequential Budgeted Learning. Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor |
| 2021 | Conformal prediction interval for dynamic time-series. Chen Xu, Yao Xie |
| 2021 | Conjugate Energy-Based Models. Hao Wu, Babak Esmaeili, Michael L. Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent |
| 2021 | Connecting Interpretability and Robustness in Decision Trees through Separation. Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri |
| 2021 | Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results. Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm |
| 2021 | Connecting Sphere Manifolds Hierarchically for Regularization. Damien Scieur, Youngsung Kim |
| 2021 | Consensus Control for Decentralized Deep Learning. Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich |
| 2021 | Conservative Objective Models for Effective Offline Model-Based Optimization. Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine |
| 2021 | Consistent Nonparametric Methods for Network Assisted Covariate Estimation. Xueyu Mao, Deepayan Chakrabarti, Purnamrita Sarkar |
| 2021 | Consistent regression when oblivious outliers overwhelm. Tommaso d'Orsi, Gleb Novikov, David Steurer |
| 2021 | Context-Aware Online Collective Inference for Templated Graphical Models. Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor |
| 2021 | Continual Learning in the Teacher-Student Setup: Impact of Task Similarity. Sebastian Lee, Sebastian Goldt, Andrew M. Saxe |
| 2021 | Continuous Coordination As a Realistic Scenario for Lifelong Learning. Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron C. Courville, Sarath Chandar |
| 2021 | Continuous-time Model-based Reinforcement Learning. Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki |
| 2021 | Contrastive Learning Inverts the Data Generating Process. Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel |
| 2021 | Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik |
| 2021 | Convex Regularization in Monte-Carlo Tree Search. Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen |
| 2021 | ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation. Mengfan Wang, Boyu Lyu, Guoqiang Yu |
| 2021 | Cooperative Exploration for Multi-Agent Deep Reinforcement Learning. Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing |
| 2021 | Correcting Exposure Bias for Link Recommendation. Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang |
| 2021 | Correlation Clustering in Constant Many Parallel Rounds. Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski |
| 2021 | CountSketches, Feature Hashing and the Median of Three. Kasper Green Larsen, Rasmus Pagh, Jakub Tetek |
| 2021 | Counterfactual Credit Assignment in Model-Free Reinforcement Learning. Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rémi Munos |
| 2021 | Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data. Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar |
| 2021 | Cross-domain Imitation from Observations. Dripta S. Raychaudhuri, Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury |
| 2021 | Cross-model Back-translated Distillation for Unsupervised Machine Translation. Xuan-Phi Nguyen, Shafiq R. Joty, Thanh-Tung Nguyen, Kui Wu, Ai Ti Aw |
| 2021 | Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization. Shahana Ibrahim, Xiao Fu |
| 2021 | Crystallization Learning with the Delaunay Triangulation. Jiaqi Gu, Guosheng Yin |
| 2021 | Cumulants of Hawkes Processes are Robust to Observation Noise. William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran |
| 2021 | Cyclically Equivariant Neural Decoders for Cyclic Codes. Xiangyu Chen, Min Ye |
| 2021 | DAGs with No Curl: An Efficient DAG Structure Learning Approach. Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji |
| 2021 | DANCE: Enhancing saliency maps using decoys. Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble |
| 2021 | DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning. Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee |
| 2021 | DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines |
| 2021 | DORO: Distributional and Outlier Robust Optimization. Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar |
| 2021 | Dash: Semi-Supervised Learning with Dynamic Thresholding. Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yufeng Li, Baigui Sun, Hao Li, Rong Jin |
| 2021 | Data Augmentation for Meta-Learning. Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein |
| 2021 | Data augmentation for deep learning based accelerated MRI reconstruction with limited data. Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi |
| 2021 | Data-Free Knowledge Distillation for Heterogeneous Federated Learning. Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou |
| 2021 | Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps. Renyi Chen, Molei Tao |
| 2021 | Data-efficient Hindsight Off-policy Option Learning. Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller |
| 2021 | Dataset Condensation with Differentiable Siamese Augmentation. Bo Zhao, Hakan Bilen |
| 2021 | Dataset Dynamics via Gradient Flows in Probability Space. David Alvarez-Melis, Nicolò Fusi |
| 2021 | Debiasing Model Updates for Improving Personalized Federated Training. Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama |
| 2021 | Debiasing a First-order Heuristic for Approximate Bi-level Optimization. Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Quincy Davis, Adrian Weller |
| 2021 | Decentralized Riemannian Gradient Descent on the Stiefel Manifold. Shixiang Chen, Alfredo García, Mingyi Hong, Shahin Shahrampour |
| 2021 | Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games. Hongyi Guo, Zuyue Fu, Zhuoran Yang, Zhaoran Wang |
| 2021 | Deciding What to Learn: A Rate-Distortion Approach. Dilip Arumugam, Benjamin Van Roy |
| 2021 | Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond. Dennis Wei |
| 2021 | Decomposable Submodular Function Minimization via Maximum Flow. Kyriakos Axiotis, Adam Karczmarz, Anish Mukherjee, Piotr Sankowski, Adrian Vladu |
| 2021 | Decomposed Mutual Information Estimation for Contrastive Representation Learning. Alessandro Sordoni, Nouha Dziri, Hannes Schulz, Geoffrey J. Gordon, Philip Bachman, Remi Tachet des Combes |
| 2021 | Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn |
| 2021 | Decoupling Representation Learning from Reinforcement Learning. Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin |
| 2021 | Decoupling Value and Policy for Generalization in Reinforcement Learning. Roberta Raileanu, Rob Fergus |
| 2021 | Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth |
| 2021 | Deep Coherent Exploration for Continuous Control. Yijie Zhang, Herke van Hoof |
| 2021 | Deep Continuous Networks. Nergis Tomen, Silvia-Laura Pintea, Jan van Gemert |
| 2021 | Deep Generative Learning via Schrödinger Bridge. Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang |
| 2021 | Deep Kernel Processes. Laurence Aitchison, Adam X. Yang, Sebastian W. Ober |
| 2021 | Deep Latent Graph Matching. Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li |
| 2021 | Deep Learning for Functional Data Analysis with Adaptive Basis Layers. Junwen Yao, Jonas Mueller, Jane-Ling Wang |
| 2021 | Deep Reinforcement Learning amidst Continual Structured Non-Stationarity. Annie Xie, James Harrison, Chelsea Finn |
| 2021 | DeepReDuce: ReLU Reduction for Fast Private Inference. Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen |
| 2021 | DeepWalking Backwards: From Embeddings Back to Graphs. Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis |
| 2021 | Deeply-Debiased Off-Policy Interval Estimation. Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song |
| 2021 | Defense against backdoor attacks via robust covariance estimation. Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh |
| 2021 | Delving into Deep Imbalanced Regression. Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Hao Wang, Dina Katabi |
| 2021 | Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation. Christopher R. Dance, Julien Perez, Théo Cachet |
| 2021 | Demystifying Inductive Biases for (Beta-)VAE Based Architectures. Dominik Zietlow, Michal Rolínek, Georg Martius |
| 2021 | Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset. Ilan Price, Jared Tanner |
| 2021 | Density Constrained Reinforcement Learning. Zengyi Qin, Yuxiao Chen, Chuchu Fan |
| 2021 | Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. Robin M. Schmidt, Frank Schneider, Philipp Hennig |
| 2021 | Detecting Rewards Deterioration in Episodic Reinforcement Learning. Ido Greenberg, Shie Mannor |
| 2021 | Detection of Signal in the Spiked Rectangular Models. Ji Hyung Jung, Hye Won Chung, Ji Oon Lee |
| 2021 | Dichotomous Optimistic Search to Quantify Human Perception. Julien Audiffren |
| 2021 | Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution. Zhaoyang Zhang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, Ping Luo |
| 2021 | Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet |
| 2021 | Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen |
| 2021 | Differentiable Spatial Planning using Transformers. Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik |
| 2021 | Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha |
| 2021 | Differentially Private Bayesian Inference for Generalized Linear Models. Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela |
| 2021 | Differentially Private Correlation Clustering. Mark Bun, Marek Eliás, Janardhan Kulkarni |
| 2021 | Differentially Private Densest Subgraph Detection. Dung Nguyen, Anil Vullikanti |
| 2021 | Differentially Private Quantiles. Jennifer Gillenwater, Matthew Joseph, Alex Kulesza |
| 2021 | Differentially Private Query Release Through Adaptive Projection. Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva |
| 2021 | Differentially Private Sliced Wasserstein Distance. Alain Rakotomamonjy, Liva Ralaivola |
| 2021 | Differentially-Private Clustering of Easy Instances. Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia |
| 2021 | Diffusion Earth Mover's Distance and Distribution Embeddings. Alexander Tong, Guillaume Huguet, Amine Natik, Kincaid MacDonald, Manik Kuchroo, Ronald R. Coifman, Guy Wolf, Smita Krishnaswamy |
| 2021 | Diffusion Source Identification on Networks with Statistical Confidence. Quinlan Dawkins, Tianxi Li, Haifeng Xu |
| 2021 | Dimensionality Reduction for the Sum-of-Distances Metric. Zhili Feng, Praneeth Kacham, David P. Woodruff |
| 2021 | Directed Graph Embeddings in Pseudo-Riemannian Manifolds. Aaron Sim, Maciej Wiatrak, Angus Brayne, Páidí Creed, Saee Paliwal |
| 2021 | Directional Bias Amplification. Angelina Wang, Olga Russakovsky |
| 2021 | Directional Graph Networks. Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Lió |
| 2021 | Disambiguation of Weak Supervision leading to Exponential Convergence rates. Vivien A. Cabannes, Francis R. Bach, Alessandro Rudi |
| 2021 | Discovering symbolic policies with deep reinforcement learning. Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol |
| 2021 | Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information. Changhun Jo, Kangwook Lee |
| 2021 | Discretization Drift in Two-Player Games. Mihaela Rosca, Yan Wu, Benoit Dherin, David Barrett |
| 2021 | Discriminative Complementary-Label Learning with Weighted Loss. Yi Gao, Min-Ling Zhang |
| 2021 | Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar |
| 2021 | Disentangling syntax and semantics in the brain with deep networks. Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King |
| 2021 | Dissecting Supervised Constrastive Learning. Florian Graf, Christoph D. Hofer, Marc Niethammer, Roland Kwitt |
| 2021 | Distributed Nyström Kernel Learning with Communications. Rong Yin, Yong Liu, Weiping Wang, Dan Meng |
| 2021 | Distributed Second Order Methods with Fast Rates and Compressed Communication. Rustem Islamov, Xun Qian, Peter Richtárik |
| 2021 | Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting. Chirag Gupta, Aaditya Ramdas |
| 2021 | Distributionally Robust Optimization with Markovian Data. Mengmeng Li, Tobias Sutter, Daniel Kuhn |
| 2021 | Ditto: Fair and Robust Federated Learning Through Personalization. Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith |
| 2021 | Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration. Seungyul Han, Youngchul Sung |
| 2021 | Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy |
| 2021 | Domain Generalization using Causal Matching. Divyat Mahajan, Shruti Tople, Amit Sharma |
| 2021 | Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. Yu Bai, Song Mei, Huan Wang, Caiming Xiong |
| 2021 | DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning. Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu |
| 2021 | Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference. Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin |
| 2021 | Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang |
| 2021 | DriftSurf: Stable-State / Reactive-State Learning under Concept Drift. Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura, Phillip B. Gibbons |
| 2021 | Dropout: Explicit Forms and Capacity Control. Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro |
| 2021 | Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach. Tianyu Ding, Zhihui Zhu, René Vidal, Daniel P. Robinson |
| 2021 | Dueling Convex Optimization. Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2021 | Dynamic Balancing for Model Selection in Bandits and RL. Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit |
| 2021 | Dynamic Game Theoretic Neural Optimizer. Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou |
| 2021 | Dynamic Planning and Learning under Recovering Rewards. David Simchi-Levi, Zeyu Zheng, Feng Zhu |
| 2021 | E(n) Equivariant Graph Neural Networks. Victor Garcia Satorras, Emiel Hoogeboom, Max Welling |
| 2021 | EL-Attention: Memory Efficient Lossless Attention for Generation. Yu Yan, Jiusheng Chen, Weizhen Qi, Nikhil Bhendawade, Yeyun Gong, Nan Duan, Ruofei Zhang |
| 2021 | EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL. Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu |
| 2021 | Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games. Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright, Michael Bowling, Amy R. Greenwald |
| 2021 | Efficient Differentiable Simulation of Articulated Bodies. Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin |
| 2021 | Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model. Christian B. Thygesen, Christian Skjødt Steenmans, Ahmad Salim Al-Sibahi, Lys Sanz Moreta, Anders Bundgård Sørensen, Thomas Hamelryck |
| 2021 | Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations. Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan |
| 2021 | Efficient Lottery Ticket Finding: Less Data is More. Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang |
| 2021 | Efficient Message Passing for 0-1 ILPs with Binary Decision Diagrams. Jan-Hendrik Lange, Paul Swoboda |
| 2021 | Efficient Online Learning for Dynamic k-Clustering. Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis |
| 2021 | Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations. Angeliki Kamoutsi, Goran Banjac, John Lygeros |
| 2021 | Efficient Statistical Tests: A Neural Tangent Kernel Approach. Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, Jimmy Ba |
| 2021 | Efficient Training of Robust Decision Trees Against Adversarial Examples. Daniël Vos, Sicco Verwer |
| 2021 | EfficientNetV2: Smaller Models and Faster Training. Mingxing Tan, Quoc V. Le |
| 2021 | EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture. Chenfeng Miao, Shuang Liang, Zhengchen Liu, Minchuan Chen, Jun Ma, Shaojun Wang, Jing Xiao |
| 2021 | Elastic Graph Neural Networks. Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang |
| 2021 | Elementary superexpressive activations. Dmitry Yarotsky |
| 2021 | Emergent Social Learning via Multi-agent Reinforcement Learning. Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques |
| 2021 | Emphatic Algorithms for Deep Reinforcement Learning. Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt |
| 2021 | End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. Syama Sundar Rangapuram, Lucien D. Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski |
| 2021 | Enhancing Robustness of Neural Networks through Fourier Stabilization. Netanel Raviv, Aidan Kelley, Minzhe Guo, Yevgeniy Vorobeychik |
| 2021 | Ensemble Bootstrapping for Q-Learning. Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir |
| 2021 | Environment Inference for Invariant Learning. Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel |
| 2021 | Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes. Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh |
| 2021 | Equivariant Networks for Pixelized Spheres. Mehran Shakerinava, Siamak Ravanbakhsh |
| 2021 | Equivariant message passing for the prediction of tensorial properties and molecular spectra. Kristof Schütt, Oliver T. Unke, Michael Gastegger |
| 2021 | Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. Yonghan Jung, Jin Tian, Elias Bareinboim |
| 2021 | Estimating α-Rank from A Few Entries with Low Rank Matrix Completion. Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang |
| 2021 | Estimation and Quantization of Expected Persistence Diagrams. Vincent Divol, Théo Lacombe |
| 2021 | Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann |
| 2021 | Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo. James A. Brofos, Roy R. Lederman |
| 2021 | Event Outlier Detection in Continuous Time. Siqi Liu, Milos Hauskrecht |
| 2021 | Evolving Attention with Residual Convolutions. Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, Jing Yu, Ce Zhang, Gao Huang, Yunhai Tong |
| 2021 | Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. Zitong Yang, Yu Bai, Song Mei |
| 2021 | Exact Optimization of Conformal Predictors via Incremental and Decremental Learning. Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi |
| 2021 | Examining and Combating Spurious Features under Distribution Shift. Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig |
| 2021 | Explainable Automated Graph Representation Learning with Hyperparameter Importance. Xin Wang, Shuyi Fan, Kun Kuang, Wenwu Zhu |
| 2021 | Explaining Time Series Predictions with Dynamic Masks. Jonathan Crabbé, Mihaela van der Schaar |
| 2021 | Explanations for Monotonic Classifiers. João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska |
| 2021 | Exploiting Shared Representations for Personalized Federated Learning. Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai |
| 2021 | Exploiting structured data for learning contagious diseases under incomplete testing. Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John V. Guttag |
| 2021 | Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. Luisa M. Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson |
| 2021 | Explore Visual Concept Formation for Image Classification. Shengzhou Xiong, Yihua Tan, Guoyou Wang |
| 2021 | Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL. Andrea Zanette |
| 2021 | Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics. Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra |
| 2021 | Exponentially Many Local Minima in Quantum Neural Networks. Xuchen You, Xiaodi Wu |
| 2021 | Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks. Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, Da Yan |
| 2021 | FILTRA: Rethinking Steerable CNN by Filter Transform. Bo Li, Qili Wang, Gim Hee Lee |
| 2021 | FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang |
| 2021 | FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning. Tianhao Zhang, Yueheng Li, Chen Wang, Guangming Xie, Zongqing Lu |
| 2021 | Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. Aditi Jha, Michael J. Morais, Jonathan W. Pillow |
| 2021 | Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees. L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi |
| 2021 | Fair Selective Classification Via Sufficiency. Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell |
| 2021 | Fairness and Bias in Online Selection. José Correa, Andrés Cristi, Paul Duetting, Ashkan Norouzi-Fard |
| 2021 | Fairness for Image Generation with Uncertain Sensitive Attributes. Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price |
| 2021 | Fairness of Exposure in Stochastic Bandits. Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims |
| 2021 | Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss. Jiali Wang, He Chen, Rujun Jiang, Xudong Li, Zihao Li |
| 2021 | Fast Projection Onto Convex Smooth Constraints. Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Y. Levy |
| 2021 | Fast Sketching of Polynomial Kernels of Polynomial Degree. Zhao Song, David P. Woodruff, Zheng Yu, Lichen Zhang |
| 2021 | Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx |
| 2021 | Fast active learning for pure exploration in reinforcement learning. Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko |
| 2021 | Fast margin maximization via dual acceleration. Ziwei Ji, Nathan Srebro, Matus Telgarsky |
| 2021 | Faster Kernel Matrix Algebra via Density Estimation. Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner |
| 2021 | Feature Clustering for Support Identification in Extreme Regions. Hamid Jalalzai, Rémi Leluc |
| 2021 | Federated Composite Optimization. Honglin Yuan, Manzil Zaheer, Sashank J. Reddi |
| 2021 | Federated Continual Learning with Weighted Inter-client Transfer. Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, Sung Ju Hwang |
| 2021 | Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang |
| 2021 | Federated Learning of User Verification Models Without Sharing Embeddings. Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling |
| 2021 | Federated Learning under Arbitrary Communication Patterns. Dmitrii Avdiukhin, Shiva Prasad Kasiviswanathan |
| 2021 | Few-Shot Conformal Prediction with Auxiliary Tasks. Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay |
| 2021 | Few-Shot Neural Architecture Search. Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo |
| 2021 | Few-shot Language Coordination by Modeling Theory of Mind. Hao Zhu, Graham Neubig, Yonatan Bisk |
| 2021 | Finding Relevant Information via a Discrete Fourier Expansion. Mohsen Heidari, Jithin K. Sreedharan, Gil I. Shamir, Wojciech Szpankowski |
| 2021 | Finding k in Latent k- polytope. Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar |
| 2021 | Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case. Liyu Chen, Haipeng Luo |
| 2021 | Finite mixture models do not reliably learn the number of components. Diana Cai, Trevor Campbell, Tamara Broderick |
| 2021 | Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm. Sajad Khodadadian, Zaiwei Chen, Siva Theja Maguluri |
| 2021 | First-Order Methods for Wasserstein Distributionally Robust MDP. Julien Grand-Clément, Christian Kroer |
| 2021 | Fixed-Parameter and Approximation Algorithms for PCA with Outliers. Yogesh Dahiya, Fedor V. Fomin, Fahad Panolan, Kirill Simonov |
| 2021 | Flow-based Attribution in Graphical Models: A Recursive Shapley Approach. Raghav Singal, George Michailidis, Hoiyi Ng |
| 2021 | Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design. Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen |
| 2021 | Follow-the-Regularized-Leader Routes to Chaos in Routing Games. Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michal Misiurewicz, Georgios Piliouras |
| 2021 | From Local Structures to Size Generalization in Graph Neural Networks. Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron |
| 2021 | From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments. Yiwei Liu, Jiamou Liu, Kaibin Wan, Zhan Qin, Zijian Zhang, Bakhadyr Khoussainov, Liehuang Zhu |
| 2021 | From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls |
| 2021 | Function Contrastive Learning of Transferable Meta-Representations. Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf |
| 2021 | Functional Space Analysis of Local GAN Convergence. Valentin Khrulkov, Artem Babenko, Ivan V. Oseledets |
| 2021 | Fundamental Tradeoffs in Distributionally Adversarial Training. Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup B. Rao, Tung Mai |
| 2021 | Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation. Renjie Zheng, Jun-Kun Chen, Mingbo Ma, Liang Huang |
| 2021 | GANMEX: One-vs-One Attributions using GAN-based Model Explainability. Sheng-Min Shih, Pin-Ju Tien, Zohar S. Karnin |
| 2021 | GBHT: Gradient Boosting Histogram Transform for Density Estimation. Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin |
| 2021 | GLSearch: Maximum Common Subgraph Detection via Learning to Search. Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang |
| 2021 | GMAC: A Distributional Perspective on Actor-Critic Framework. Daniel Wontae Nam, Younghoon Kim, Chan Y. Park |
| 2021 | GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings. Matthias Fey, Jan Eric Lenssen, Frank Weichert, Jure Leskovec |
| 2021 | GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya |
| 2021 | GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training. Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh K. Iyer |
| 2021 | GRAND: Graph Neural Diffusion. Ben Chamberlain, James Rowbottom, Maria I. Gorinova, Michael M. Bronstein, Stefan Webb, Emanuele Rossi |
| 2021 | Gaussian Process-Based Real-Time Learning for Safety Critical Applications. Armin Lederer, Alejandro Jose Ordóñez Conejo, Korbinian Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche |
| 2021 | Generalised Lipschitz Regularisation Equals Distributional Robustness. Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith |
| 2021 | Generalizable Episodic Memory for Deep Reinforcement Learning. Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang |
| 2021 | Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon |
| 2021 | Generalization Error Bound for Hyperbolic Ordinal Embedding. Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza |
| 2021 | Generalization Guarantees for Neural Architecture Search with Train-Validation Split. Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi |
| 2021 | Generalized Doubly Reparameterized Gradient Estimators. Matthias Bauer, Andriy Mnih |
| 2021 | Generating images with sparse representations. Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia |
| 2021 | Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation. Sung Woo Park, Dong Wook Shu, Junseok Kwon |
| 2021 | Generative Adversarial Transformers. Drew A. Hudson, Larry Zitnick |
| 2021 | Generative Causal Explanations for Graph Neural Networks. Wanyu Lin, Hao Lan, Baochun Li |
| 2021 | Generative Particle Variational Inference via Estimation of Functional Gradients. Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu |
| 2021 | Generative Video Transformer: Can Objects be the Words? Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn |
| 2021 | GeomCA: Geometric Evaluation of Data Representations. Petra Poklukar, Anastasiia Varava, Danica Kragic |
| 2021 | Geometric convergence of elliptical slice sampling. Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk |
| 2021 | Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clément Hongler, Wulfram Gerstner, Johanni Brea |
| 2021 | Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time. Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang |
| 2021 | Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs. Tolga Ergen, Mert Pilanci |
| 2021 | Global Prosody Style Transfer Without Text Transcriptions. Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David D. Cox, Mark Hasegawa-Johnson |
| 2021 | Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. Sebastian W. Ober, Laurence Aitchison |
| 2021 | Globally-Robust Neural Networks. Klas Leino, Zifan Wang, Matt Fredrikson |
| 2021 | Goal-Conditioned Reinforcement Learning with Imagined Subgoals. Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev |
| 2021 | Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech. Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail A. Kudinov |
| 2021 | Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher |
| 2021 | Graph Contrastive Learning Automated. Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang |
| 2021 | Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization. Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath |
| 2021 | Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2021 | Graph Mixture Density Networks. Federico Errica, Davide Bacciu, Alessio Micheli |
| 2021 | Graph Neural Networks Inspired by Classical Iterative Algorithms. Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf |
| 2021 | GraphDF: A Discrete Flow Model for Molecular Graph Generation. Youzhi Luo, Keqiang Yan, Shuiwang Ji |
| 2021 | GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training. Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang |
| 2021 | Grey-box Extraction of Natural Language Models. Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf |
| 2021 | Grid-Functioned Neural Networks. Javier Dehesa, Andrew Vidler, Julian A. Padget, Christof Lutteroth |
| 2021 | Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning. Austin W. Hanjie, Victor Zhong, Karthik Narasimhan |
| 2021 | Group Fisher Pruning for Practical Network Compression. Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue, Xinjiang Wang, Yimin Chen, Wenming Yang, Qingmin Liao, Wayne Zhang |
| 2021 | Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings. Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani |
| 2021 | Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge |
| 2021 | Guided Exploration with Proximal Policy Optimization using a Single Demonstration. Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert |
| 2021 | HAWQ-V3: Dyadic Neural Network Quantization. Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael W. Mahoney, Kurt Keutzer |
| 2021 | HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. Qian Lou, Lei Jiang |
| 2021 | HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search. Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik |
| 2021 | Heterogeneity for the Win: One-Shot Federated Clustering. Don Kurian Dennis, Tian Li, Virginia Smith |
| 2021 | Heterogeneous Risk Minimization. Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen |
| 2021 | Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time. Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni, Jessica Shi |
| 2021 | Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees. Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M. Procopiuc, Claudio Gentile |
| 2021 | Hierarchical VAEs Know What They Don't Know. Jakob Drachmann Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe |
| 2021 | High Confidence Generalization for Reinforcement Learning. James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas |
| 2021 | High-Dimensional Gaussian Process Inference with Derivatives. Filip de Roos, Alexandra Gessner, Philipp Hennig |
| 2021 | High-Performance Large-Scale Image Recognition Without Normalization. Andy Brock, Soham De, Samuel L. Smith, Karen Simonyan |
| 2021 | High-dimensional Experimental Design and Kernel Bandits. Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels |
| 2021 | Homomorphic Sensing: Sparsity and Noise. Liangzu Peng, Boshi Wang, Manolis C. Tsakiris |
| 2021 | HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections. Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré |
| 2021 | Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers. Jyotikrishna Dass, Rabi N. Mahapatra |
| 2021 | How Do Adam and Training Strategies Help BNNs Optimization. Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng |
| 2021 | How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation. Ali Akbari, Muhammad Awais, Manijeh Bashar, Josef Kittler |
| 2021 | How Framelets Enhance Graph Neural Networks. Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yuguang Wang, Pietro Lió, Ming Li, Guido Montúfar |
| 2021 | How Important is the Train-Validation Split in Meta-Learning? Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong |
| 2021 | How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference. Amanda Gentzel, Purva Pruthi, David D. Jensen |
| 2021 | How could Neural Networks understand Programs? Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu |
| 2021 | How rotational invariance of common kernels prevents generalization in high dimensions. Konstantin Donhauser, Mingqi Wu, Fanny Yang |
| 2021 | How to Learn when Data Reacts to Your Model: Performative Gradient Descent. Zachary Izzo, Lexing Ying, James Zou |
| 2021 | HyperHyperNetwork for the Design of Antenna Arrays. Shahar Lutati, Lior Wolf |
| 2021 | Hyperparameter Selection for Imitation Learning. Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphaël Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin |
| 2021 | I-BERT: Integer-only BERT Quantization. Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer |
| 2021 | Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Animashree Anandkumar, Sanja Fidler, José M. Álvarez |
| 2021 | Imitation by Predicting Observations. Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne |
| 2021 | Implicit Bias of Linear RNNs. Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher |
| 2021 | Implicit Regularization in Tensor Factorization. Noam Razin, Asaf Maman, Nadav Cohen |
| 2021 | Implicit rate-constrained optimization of non-decomposable objectives. Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter |
| 2021 | Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold. Kieran A. Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia |
| 2021 | Improved Algorithms for Agnostic Pool-based Active Classification. Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson |
| 2021 | Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason |
| 2021 | Improved Contrastive Divergence Training of Energy-Based Models. Yilun Du, Shuang Li, Joshua B. Tenenbaum, Igor Mordatch |
| 2021 | Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. Yifang Chen, Simon S. Du, Kevin Jamieson |
| 2021 | Improved Denoising Diffusion Probabilistic Models. Alexander Quinn Nichol, Prafulla Dhariwal |
| 2021 | Improved OOD Generalization via Adversarial Training and Pretraing. Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhiming Ma |
| 2021 | Improved Regret Bound and Experience Replay in Regularized Policy Iteration. Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári |
| 2021 | Improved Regret Bounds of Bilinear Bandits using Action Space Analysis. Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang |
| 2021 | Improved, Deterministic Smoothing for L Alexander Levine, Soheil Feizi |
| 2021 | Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies. Denis Lukovnikov, Asja Fischer |
| 2021 | Improving Generalization in Meta-learning via Task Augmentation. Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li |
| 2021 | Improving Gradient Regularization using Complex-Valued Neural Networks. Eric C. Yeats, Yiran Chen, Hai Li |
| 2021 | Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison |
| 2021 | Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity. Ryan Henderson, Djork-Arné Clevert, Floriane Montanari |
| 2021 | Improving Predictors via Combination Across Diverse Task Categories. Kwang In Kim |
| 2021 | Improving Ultrametrics Embeddings Through Coresets. Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde |
| 2021 | In-Database Regression in Input Sparsity Time. Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye |
| 2021 | Incentivized Bandit Learning with Self-Reinforcing User Preferences. Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng |
| 2021 | Incentivizing Compliance with Algorithmic Instruments. Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu |
| 2021 | Inference for Network Regression Models with Community Structure. Mengjie Pan, Tyler H. McCormick, Bailey K. Fosdick |
| 2021 | Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations. Timothy Doyeon Kim, Thomas Zhihao Luo, Jonathan W. Pillow, Carlos D. Brody |
| 2021 | Inferring serial correlation with dynamic backgrounds. Song Wei, Yao Xie, Dobromir Rahnev |
| 2021 | Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport. Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang |
| 2021 | Information Obfuscation of Graph Neural Networks. Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov |
| 2021 | Instabilities of Offline RL with Pre-Trained Neural Representation. Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade |
| 2021 | Instance Specific Approximations for Submodular Maximization. Eric Balkanski, Sharon Qian, Yaron Singer |
| 2021 | Instance-Optimal Compressed Sensing via Posterior Sampling. Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price |
| 2021 | Integer Programming for Causal Structure Learning in the Presence of Latent Variables. Rui Chen, Sanjeeb Dash, Tian Gao |
| 2021 | Integrated Defense for Resilient Graph Matching. Jiaxiang Ren, Zijie Zhang, Jiayin Jin, Xin Zhao, Sixing Wu, Yang Zhou, Yelong Shen, Tianshi Che, Ruoming Jin, Dejing Dou |
| 2021 | Interaction-Grounded Learning. Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad |
| 2021 | Interactive Learning from Activity Description. Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudík, Patrick Shafto |
| 2021 | Intermediate Layer Optimization for Inverse Problems using Deep Generative Models. Giannis Daras, Joseph Dean, Ajil Jalal, Alex Dimakis |
| 2021 | Interpretable Stability Bounds for Spectral Graph Filters. Henry Kenlay, Dorina Thanou, Xiaowen Dong |
| 2021 | Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold. Wenkai Xu, Takeru Matsuda |
| 2021 | Interpreting and Disentangling Feature Components of Various Complexity from DNNs. Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang |
| 2021 | Inverse Constrained Reinforcement Learning. Shehryar Malik, Usman Anwar, Alireza Aghasi, Ali Ahmed |
| 2021 | Inverse Decision Modeling: Learning Interpretable Representations of Behavior. Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar |
| 2021 | Is Pessimism Provably Efficient for Offline RL? Ying Jin, Zhuoran Yang, Zhaoran Wang |
| 2021 | Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius, Heng Wang, Lorenzo Torresani |
| 2021 | Isometric Gaussian Process Latent Variable Model for Dissimilarity Data. Martin Jørgensen, Søren Hauberg |
| 2021 | Joining datasets via data augmentation in the label space for neural networks. Junbo Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen |
| 2021 | Joint Online Learning and Decision-making via Dual Mirror Descent. Alfonso Lobos, Paul Grigas, Zheng Wen |
| 2021 | Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein |
| 2021 | Just Train Twice: Improving Group Robustness without Training Group Information. Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn |
| 2021 | K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets. Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu |
| 2021 | KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, Wei Chen |
| 2021 | KNAS: Green Neural Architecture Search. Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, Hongxia Yang |
| 2021 | KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning. Ashok Vardhan Makkuva, Xiyang Liu, Mohammad Vahid Jamali, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath |
| 2021 | Kernel Continual Learning. Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees Snoek |
| 2021 | Kernel Stein Discrepancy Descent. Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin |
| 2021 | Kernel-Based Reinforcement Learning: A Finite-Time Analysis. Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | Keyframe-Focused Visual Imitation Learning. Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman |
| 2021 | Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks. Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li |
| 2021 | LAMDA: Label Matching Deep Domain Adaptation. Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, Dinh Phung |
| 2021 | LARNet: Lie Algebra Residual Network for Face Recognition. Xiaolong Yang, Xiaohong Jia, Dihong Gong, Dong-Ming Yan, Zhifeng Li, Wei Liu |
| 2021 | LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou |
| 2021 | LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, Christian Szegedy |
| 2021 | LTL2Action: Generalizing LTL Instructions for Multi-Task RL. Pashootan Vaezipoor, Andrew C. Li, Rodrigo Toro Icarte, Sheila A. McIlraith |
| 2021 | Label Distribution Learning Machine. Jing Wang, Xin Geng |
| 2021 | Label Inference Attacks from Log-loss Scores. Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier |
| 2021 | Label-Only Membership Inference Attacks. Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot |
| 2021 | Large Scale Private Learning via Low-rank Reparametrization. Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu |
| 2021 | Large-Margin Contrastive Learning with Distance Polarization Regularizer. Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama |
| 2021 | Large-Scale Meta-Learning with Continual Trajectory Shifting. Jaewoong Shin, Haebeom Lee, Boqing Gong, Sung Ju Hwang |
| 2021 | Large-Scale Multi-Agent Deep FBSDEs. Tianrong Chen, Ziyi Wang, Ioannis Exarchos, Evangelos A. Theodorou |
| 2021 | Latent Programmer: Discrete Latent Codes for Program Synthesis. Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer |
| 2021 | Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification. Bo Pang, Ying Nian Wu |
| 2021 | Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing. Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao |
| 2021 | Learn2Hop: Learned Optimization on Rough Landscapes. Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin D. Cubuk |
| 2021 | Learner-Private Convex Optimization. Jiaming Xu, Kuang Xu, Dana Yang |
| 2021 | Learning Binary Decision Trees by Argmin Differentiation. Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae |
| 2021 | Learning Bounds for Open-Set Learning. Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang |
| 2021 | Learning Curves for Analysis of Deep Networks. Derek Hoiem, Tanmay Gupta, Zhizhong Li, Michal Shlapentokh-Rothman |
| 2021 | Learning Deep Neural Networks under Agnostic Corrupted Supervision. Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou |
| 2021 | Learning Diverse-Structured Networks for Adversarial Robustness. Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama |
| 2021 | Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning. Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng |
| 2021 | Learning Generalized Intersection Over Union for Dense Pixelwise Prediction. Jiaqian Yu, Jingtao Xu, Yiwei Chen, Weiming Li, Qiang Wang, ByungIn Yoo, Jae-Joon Han |
| 2021 | Learning Gradient Fields for Molecular Conformation Generation. Chence Shi, Shitong Luo, Minkai Xu, Jian Tang |
| 2021 | Learning Interaction Kernels for Agent Systems on Riemannian Manifolds. Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong |
| 2021 | Learning Intra-Batch Connections for Deep Metric Learning. Jenny Denise Seidenschwarz, Ismail Elezi, Laura Leal-Taixé |
| 2021 | Learning Neural Network Subspaces. Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari |
| 2021 | Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks. Ciwan Ceylan, Salla Franzén, Florian T. Pokorny |
| 2021 | Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. Yivan Zhang, Gang Niu, Masashi Sugiyama |
| 2021 | Learning Online Algorithms with Distributional Advice. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis |
| 2021 | Learning Optimal Auctions with Correlated Valuations from Samples. Chunxue Yang, Xiaohui Bei |
| 2021 | Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis. Jeroen Berrevoets, Ahmed M. Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar |
| 2021 | Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization. Hedda Cohen Indelman, Tamir Hazan |
| 2021 | Learning Representations by Humans, for Humans. Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, David C. Parkes |
| 2021 | Learning Routines for Effective Off-Policy Reinforcement Learning. Edoardo Cetin, Oya Çeliktutan |
| 2021 | Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation. Chao Chen, Haoyu Geng, Nianzu Yang, Junchi Yan, Daiyue Xue, Jianping Yu, Xiaokang Yang |
| 2021 | Learning Stochastic Behaviour from Aggregate Data. Shaojun Ma, Shu Liu, Hongyuan Zha, Haomin Zhou |
| 2021 | Learning Task Informed Abstractions. Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola |
| 2021 | Learning Transferable Visual Models From Natural Language Supervision. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever |
| 2021 | Learning While Playing in Mean-Field Games: Convergence and Optimality. Qiaomin Xie, Zhuoran Yang, Zhaoran Wang, Andreea Minca |
| 2021 | Learning a Universal Template for Few-shot Dataset Generalization. Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin |
| 2021 | Learning and Planning in Average-Reward Markov Decision Processes. Yi Wan, Abhishek Naik, Richard S. Sutton |
| 2021 | Learning and Planning in Complex Action Spaces. Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver |
| 2021 | Learning by Turning: Neural Architecture Aware Optimisation. Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue |
| 2021 | Learning de-identified representations of prosody from raw audio. Jack Weston, Raphael Lenain, Udeepa Meepegama, Emil Fristed |
| 2021 | Learning disentangled representations via product manifold projection. Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodolà |
| 2021 | Learning from Biased Data: A Semi-Parametric Approach. Patrice Bertail, Stéphan Clémençon, Yannick Guyonvarch, Nathan Noiry |
| 2021 | Learning from History for Byzantine Robust Optimization. Sai Praneeth Karimireddy, Lie He, Martin Jaggi |
| 2021 | Learning from Nested Data with Ornstein Auto-Encoders. Youngwon Choi, Sungdong Lee, Joong-Ho Won |
| 2021 | Learning from Noisy Labels with No Change to the Training Process. Mingyuan Zhang, Jane H. Lee, Shivani Agarwal |
| 2021 | Learning from Similarity-Confidence Data. Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama |
| 2021 | Learning in Nonzero-Sum Stochastic Games with Potentials. David Henry Mguni, Yutong Wu, Yali Du, Yaodong Yang, Ziyi Wang, Minne Li, Ying Wen, Joel Jennings, Jun Wang |
| 2021 | Learning to Generate Noise for Multi-Attack Robustness. Divyam Madaan, Jinwoo Shin, Sung Ju Hwang |
| 2021 | Learning to Price Against a Moving Target. Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah |
| 2021 | Learning to Rehearse in Long Sequence Memorization. Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao |
| 2021 | Learning to Weight Imperfect Demonstrations. Yunke Wang, Chang Xu, Bo Du, Honglak Lee |
| 2021 | Lenient Regret and Good-Action Identification in Gaussian Process Bandits. Xu Cai, Selwyn Gomes, Jonathan Scarlett |
| 2021 | Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework. Floris Geerts, Filip Mazowiecki, Guillermo A. Pérez |
| 2021 | Leveraged Weighted Loss for Partial Label Learning. Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin |
| 2021 | Leveraging Good Representations in Linear Contextual Bandits. Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta |
| 2021 | Leveraging Language to Learn Program Abstractions and Search Heuristics. Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas |
| 2021 | Leveraging Non-uniformity in First-order Non-convex Optimization. Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans |
| 2021 | Leveraging Public Data for Practical Private Query Release. Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2021 | Leveraging Sparse Linear Layers for Debuggable Deep Networks. Eric Wong, Shibani Santurkar, Aleksander Madry |
| 2021 | LieTransformer: Equivariant Self-Attention for Lie Groups. Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim |
| 2021 | Light RUMs. Flavio Chierichetti, Ravi Kumar, Andrew Tomkins |
| 2021 | Linear Transformers Are Secretly Fast Weight Programmers. Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber |
| 2021 | Link Prediction with Persistent Homology: An Interactive View. Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen |
| 2021 | Lipschitz normalization for self-attention layers with application to graph neural networks. George Dasoulas, Kevin Scaman, Aladin Virmaux |
| 2021 | Local Algorithms for Finding Densely Connected Clusters. Peter Macgregor, He Sun |
| 2021 | Local Correlation Clustering with Asymmetric Classification Errors. Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev |
| 2021 | Locally Adaptive Label Smoothing Improves Predictive Churn. Dara Bahri, Heinrich Jiang |
| 2021 | Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards. Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup |
| 2021 | Locally Private k-Means in One Round. Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi |
| 2021 | LogME: Practical Assessment of Pre-trained Models for Transfer Learning. Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long |
| 2021 | Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson |
| 2021 | Lossless Compression of Efficient Private Local Randomizers. Vitaly Feldman, Kunal Talwar |
| 2021 | Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not? Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, Yanzhi Wang |
| 2021 | Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision. Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger |
| 2021 | Low-Rank Sinkhorn Factorization. Meyer Scetbon, Marco Cuturi, Gabriel Peyré |
| 2021 | Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal |
| 2021 | Lower-Bounded Proper Losses for Weakly Supervised Classification. Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama |
| 2021 | MARINA: Faster Non-Convex Distributed Learning with Compression. Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik |
| 2021 | MC-LSTM: Mass-Conserving LSTM. Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer |
| 2021 | MOTS: Minimax Optimal Thompson Sampling. Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu |
| 2021 | MSA Transformer. Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives |
| 2021 | MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine |
| 2021 | Machine Unlearning for Random Forests. Jonathan Brophy, Daniel Lowd |
| 2021 | Making Paper Reviewing Robust to Bid Manipulation Attacks. Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger |
| 2021 | Making transport more robust and interpretable by moving data through a small number of anchor points. Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer |
| 2021 | Mandoline: Model Evaluation under Distribution Shift. Mayee F. Chen, Karan Goel, Nimit Sharad Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Ré |
| 2021 | Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. Amnon Catav, Boyang Fu, Yazeed Zoabi, Ahuva Weiss-Meilik, Noam Shomron, Jason Ernst, Sriram Sankararaman, Ran Gilad-Bachrach |
| 2021 | Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. Geng Ji, Debora Sujono, Erik B. Sudderth |
| 2021 | Markpainting: Adversarial Machine Learning meets Inpainting. David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson |
| 2021 | Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling. Kuruge Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Rahimi Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan Kumar Yadav |
| 2021 | Matrix Completion with Model-free Weighting. Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan |
| 2021 | Matrix Sketching for Secure Collaborative Machine Learning. Mengjiao Zhang, Shusen Wang |
| 2021 | Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama |
| 2021 | Measuring Robustness in Deep Learning Based Compressive Sensing. Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel |
| 2021 | Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama |
| 2021 | Megaverse: Simulating Embodied Agents at One Million Experiences per Second. Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, Vladlen Koltun |
| 2021 | Memory Efficient Online Meta Learning. Durmus Alp Emre Acar, Ruizhao Zhu, Venkatesh Saligrama |
| 2021 | Memory-Efficient Pipeline-Parallel DNN Training. Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia |
| 2021 | Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning. TaeHyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J. Lim, Byoung-Tak Zhang |
| 2021 | Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation. Qian Zhang, Yilin Zheng, Jean Honorio |
| 2021 | Meta-Cal: Well-controlled Post-hoc Calibration by Ranking. Xingchen Ma, Matthew B. Blaschko |
| 2021 | Meta-Learning Bidirectional Update Rules. Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas |
| 2021 | Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Dongchan Min, Dong Bok Lee, Eunho Yang, Sung Ju Hwang |
| 2021 | Meta-Thompson Sampling. Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári |
| 2021 | Meta-learning Hyperparameter Performance Prediction with Neural Processes. Ying Wei, Peilin Zhao, Junzhou Huang |
| 2021 | MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration. Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang |
| 2021 | Mind the Box: l Francesco Croce, Matthias Hein |
| 2021 | Mixed Cross Entropy Loss for Neural Machine Translation. Haoran Li, Wei Lu |
| 2021 | Mixed Nash Equilibria in the Adversarial Examples Game. Laurent Meunier, Meyer Scetbon, Rafael Pinot, Jamal Atif, Yann Chevaleyre |
| 2021 | Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. Max Biggs, Wei Sun, Markus Ettl |
| 2021 | Model Fusion for Personalized Learning. Thanh Chi Lam, Trong Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | Model Performance Scaling with Multiple Data Sources. Tatsunori Hashimoto |
| 2021 | Model-Based Reinforcement Learning via Latent-Space Collocation. Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine |
| 2021 | Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity. Zihan Zhang, Yuan Zhou, Xiangyang Ji |
| 2021 | Model-Free and Model-Based Policy Evaluation when Causality is Uncertain. David Bruns-Smith |
| 2021 | Model-Targeted Poisoning Attacks with Provable Convergence. Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian |
| 2021 | Model-based Reinforcement Learning for Continuous Control with Posterior Sampling. Ying Fan, Yifei Ming |
| 2021 | Modeling Hierarchical Structures with Continuous Recursive Neural Networks. Jishnu Ray Chowdhury, Cornelia Caragea |
| 2021 | Modelling Behavioural Diversity for Learning in Open-Ended Games. Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers, David Henry Mguni, Ying Wen, Jun Wang |
| 2021 | Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths |
| 2021 | Momentum Residual Neural Networks. Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré |
| 2021 | Monotonic Robust Policy Optimization with Model Discrepancy. Yuankun Jiang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong |
| 2021 | Monte Carlo Variational Auto-Encoders. Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov |
| 2021 | More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method. Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi |
| 2021 | Moreau-Yosida f-divergences. Dávid Terjék |
| 2021 | MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space. Sophie Laturnus, Philipp Berens |
| 2021 | Muesli: Combining Improvements in Policy Optimization. Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt |
| 2021 | Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel |
| 2021 | Multi-Dimensional Classification via Sparse Label Encoding. Bin-Bin Jia, Min-Ling Zhang |
| 2021 | Multi-Receiver Online Bayesian Persuasion. Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti |
| 2021 | Multi-Task Reinforcement Learning with Context-based Representations. Shagun Sodhani, Amy Zhang, Joelle Pineau |
| 2021 | Multi-group Agnostic PAC Learnability. Guy N. Rothblum, Gal Yona |
| 2021 | Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning. Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C. S. Lui |
| 2021 | Multidimensional Scaling: Approximation and Complexity. Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel |
| 2021 | Multiplicative Noise and Heavy Tails in Stochastic Optimization. Liam Hodgkinson, Michael W. Mahoney |
| 2021 | Multiplying Matrices Without Multiplying. Davis W. Blalock, John V. Guttag |
| 2021 | Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference. Shumao Zhang, Pengchuan Zhang, Thomas Y. Hou |
| 2021 | Narrow Margins: Classification, Margins and Fat Tails. Francois Buet-Golfouse |
| 2021 | Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann |
| 2021 | NeRF-VAE: A Geometry Aware 3D Scene Generative Model. Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo Jimenez Rezende |
| 2021 | Near Optimal Reward-Free Reinforcement Learning. Zihan Zhang, Simon S. Du, Xiangyang Ji |
| 2021 | Near-Optimal Algorithms for Explainable k-Medians and k-Means. Konstantin Makarychev, Liren Shan |
| 2021 | Near-Optimal Confidence Sequences for Bounded Random Variables. Arun K. Kuchibhotla, Qinqing Zheng |
| 2021 | Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise. Vivek F. Farias, Andrew A. Li, Tianyi Peng |
| 2021 | Near-Optimal Linear Regression under Distribution Shift. Qi Lei, Wei Hu, Jason D. Lee |
| 2021 | Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs. Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar |
| 2021 | Near-Optimal Representation Learning for Linear Bandits and Linear RL. Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang |
| 2021 | Necessary and sufficient conditions for causal feature selection in time series with latent common causes. Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing |
| 2021 | Neighborhood Contrastive Learning Applied to Online Patient Monitoring. Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch |
| 2021 | Network Inference and Influence Maximization from Samples. Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang |
| 2021 | Neural Architecture Search without Training. Joe Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley |
| 2021 | Neural Feature Matching in Implicit 3D Representations. Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves |
| 2021 | Neural Pharmacodynamic State Space Modeling. Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag |
| 2021 | Neural Rough Differential Equations for Long Time Series. James Morrill, Cristopher Salvi, Patrick Kidger, James Foster |
| 2021 | Neural SDEs as Infinite-Dimensional GANs. Patrick Kidger, James Foster, Xuechen Li, Terry J. Lyons |
| 2021 | Neural Symbolic Regression that scales. Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo |
| 2021 | Neural Tangent Generalization Attacks. Chia-Hung Yuan, Shan-Hung Wu |
| 2021 | Neural Transformation Learning for Deep Anomaly Detection Beyond Images. Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph |
| 2021 | Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface. Baorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker |
| 2021 | Neuro-algorithmic Policies Enable Fast Combinatorial Generalization. Marin Vlastelica P., Michal Rolínek, Georg Martius |
| 2021 | Newton Method over Networks is Fast up to the Statistical Precision. Amir Daneshmand, Gesualdo Scutari, Pavel E. Dvurechensky, Alexander V. Gasnikov |
| 2021 | No-regret Algorithms for Capturing Events in Poisson Point Processes. Mojmir Mutny, Andreas Krause |
| 2021 | Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent. Kangqiao Liu, Liu Ziyin, Masahito Ueda |
| 2021 | Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo |
| 2021 | Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions. Pierre Alquier |
| 2021 | Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. Masahiro Kato, Takeshi Teshima |
| 2021 | Nondeterminism and Instability in Neural Network Optimization. Cecilia Summers, Michael J. Dinneen |
| 2021 | Nonmyopic Multifidelity Acitve Search. Quan Nguyen, Arghavan Modiri, Roman Garnett |
| 2021 | Nonparametric Decomposition of Sparse Tensors. Conor Tillinghast, Shandian Zhe |
| 2021 | Nonparametric Hamiltonian Monte Carlo. Carol Mak, Fabian Zaiser, Luke Ong |
| 2021 | Not All Memories are Created Equal: Learning to Forget by Expiring. Sainbayar Sukhbaatar, Da Ju, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan |
| 2021 | Object Segmentation Without Labels with Large-Scale Generative Models. Andrey Voynov, Stanislav Morozov, Artem Babenko |
| 2021 | Objective Bound Conditional Gaussian Process for Bayesian Optimization. Taewon Jeong, Heeyoung Kim |
| 2021 | Oblivious Sketching for Logistic Regression. Alexander Munteanu, Simon Omlor, David P. Woodruff |
| 2021 | Oblivious Sketching-based Central Path Method for Linear Programming. Zhao Song, Zheng Yu |
| 2021 | Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap. Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu |
| 2021 | Off-Belief Learning. Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, Noam Brown, Jakob N. Foerster |
| 2021 | Off-Policy Confidence Sequences. Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas |
| 2021 | Offline Contextual Bandits with Overparameterized Models. David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna |
| 2021 | Offline Meta-Reinforcement Learning with Advantage Weighting. Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn |
| 2021 | Offline Reinforcement Learning with Fisher Divergence Critic Regularization. Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum |
| 2021 | Offline Reinforcement Learning with Pseudometric Learning. Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist |
| 2021 | OmniNet: Omnidirectional Representations from Transformers. Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Prakash Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler |
| 2021 | On Characterizing GAN Convergence Through Proximal Duality Gap. Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan |
| 2021 | On Disentangled Representations Learned from Correlated Data. Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer |
| 2021 | On Energy-Based Models with Overparametrized Shallow Neural Networks. Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna |
| 2021 | On Estimation in Latent Variable Models. Guanhua Fang, Ping Li |
| 2021 | On Explainability of Graph Neural Networks via Subgraph Explorations. Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji |
| 2021 | On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. Shunta Akiyama, Taiji Suzuki |
| 2021 | On Limited-Memory Subsampling Strategies for Bandits. Dorian Baudry, Yoan Russac, Olivier Cappé |
| 2021 | On Linear Identifiability of Learned Representations. Geoffrey Roeder, Luke Metz, Durk Kingma |
| 2021 | On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization. Xu Cai, Jonathan Scarlett |
| 2021 | On Monotonic Linear Interpolation of Neural Network Parameters. James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse |
| 2021 | On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework. Zeyu Yan, Fei Wen, Rendong Ying, Chao Ma, Peilin Liu |
| 2021 | On Proximal Policy Optimization's Heavy-tailed Gradients. Saurabh Garg, Joshua Zhanson, Emilio Parisotto, Adarsh Prasad, J. Zico Kolter, Zachary C. Lipton, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Pradeep Ravikumar |
| 2021 | On Recovering from Modeling Errors Using Testing Bayesian Networks. Haiying Huang, Adnan Darwiche |
| 2021 | On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP. Tianhao Wu, Yunchang Yang, Simon S. Du, Liwei Wang |
| 2021 | On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game. Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang |
| 2021 | On Robust Mean Estimation under Coordinate-level Corruption. Zifan Liu, Jongho Park, Theodoros Rekatsinas, Christos Tzamos |
| 2021 | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth |
| 2021 | On Variational Inference in Biclustering Models. Guanhua Fang, Ping Li |
| 2021 | On a Combination of Alternating Minimization and Nesterov's Momentum. Sergey Guminov, Pavel E. Dvurechensky, Nazarii Tupitsa, Alexander V. Gasnikov |
| 2021 | On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. Difan Zou, Quanquan Gu |
| 2021 | On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks. Hancheng Min, Salma Tarmoun, René Vidal, Enrique Mallada |
| 2021 | On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models. Peizhong Ju, Xiaojun Lin, Ness B. Shroff |
| 2021 | On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry |
| 2021 | On the Inherent Regularization Effects of Noise Injection During Training. Oussama Dhifallah, Yue M. Lu |
| 2021 | On the Optimality of Batch Policy Optimization Algorithms. Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans |
| 2021 | On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise. Jie Shen |
| 2021 | On the Predictability of Pruning Across Scales. Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit |
| 2021 | On the Problem of Underranking in Group-Fair Ranking. Sruthi Gorantla, Amit Deshpande, Anand Louis |
| 2021 | On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths. Quynh Nguyen |
| 2021 | On the Random Conjugate Kernel and Neural Tangent Kernel. Zhengmian Hu, Heng Huang |
| 2021 | On the difficulty of unbiased alpha divergence minimization. Tomas Geffner, Justin Domke |
| 2021 | On the price of explainability for some clustering problems. Eduardo Sany Laber, Lucas Murtinho |
| 2021 | On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification. Zahra Babaiee, Ramin M. Hasani, Mathias Lechner, Daniela Rus, Radu Grosu |
| 2021 | On-Policy Deep Reinforcement Learning for the Average-Reward Criterion. Yiming Zhang, Keith W. Ross |
| 2021 | On-the-fly Rectification for Robust Large-Vocabulary Topic Inference. Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel |
| 2021 | One Pass Late Fusion Multi-view Clustering. Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, En Zhu |
| 2021 | One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao |
| 2021 | One-sided Frank-Wolfe algorithms for saddle problems. Vladimir Kolmogorov, Thomas Pock |
| 2021 | Oneshot Differentially Private Top-k Selection. Gang Qiao, Weijie J. Su, Li Zhang |
| 2021 | Online A-Optimal Design and Active Linear Regression. Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet |
| 2021 | Online Graph Dictionary Learning. Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty |
| 2021 | Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games. Ilai Bistritz, Nicholas Bambos |
| 2021 | Online Learning in Unknown Markov Games. Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra |
| 2021 | Online Learning with Optimism and Delay. Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey |
| 2021 | Online Limited Memory Neural-Linear Bandits with Likelihood Matching. Ofir Nabati, Tom Zahavy, Shie Mannor |
| 2021 | Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence. Yun Kuen Cheung, Georgios Piliouras |
| 2021 | Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret. Asaf B. Cassel, Tomer Koren |
| 2021 | Online Selection Problems against Constrained Adversary. Zhihao Jiang, Pinyan Lu, Zhihao Gavin Tang, Yuhao Zhang |
| 2021 | Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems. Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour |
| 2021 | Online Unrelated Machine Load Balancing with Predictions Revisited. Shi Li, Jiayi Xian |
| 2021 | Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison |
| 2021 | Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics. Avik Pal, Yingbo Ma, Viral B. Shah, Christopher Vincent Rackauckas |
| 2021 | Operationalizing Complex Causes: A Pragmatic View of Mediation. Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva |
| 2021 | OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation. Jongmin Lee, Wonseok Jeon, Byung-Jun Lee, Joelle Pineau, Kee-Eung Kim |
| 2021 | Optimal Complexity in Decentralized Training. Yucheng Lu, Christopher De Sa |
| 2021 | Optimal Counterfactual Explanations in Tree Ensembles. Axel Parmentier, Thibaut Vidal |
| 2021 | Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations. Fan Zhou, Ping Li |
| 2021 | Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method. Peng Wang, Huikang Liu, Zirui Zhou, Anthony Man-Cho So |
| 2021 | Optimal Off-Policy Evaluation from Multiple Logging Policies. Nathan Kallus, Yuta Saito, Masatoshi Uehara |
| 2021 | Optimal Streaming Algorithms for Multi-Armed Bandits. Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao |
| 2021 | Optimal Thompson Sampling strategies for support-aware CVaR bandits. Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard |
| 2021 | Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search. Vu Nguyen, Tam Le, Makoto Yamada, Michael A. Osborne |
| 2021 | Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain |
| 2021 | Optimization Planning for 3D ConvNets. Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Tao Mei |
| 2021 | Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi |
| 2021 | Optimizing Black-box Metrics with Iterative Example Weighting. Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Sanmi Koyejo |
| 2021 | Optimizing persistent homology based functions. Mathieu Carrière, Frédéric Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda |
| 2021 | Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu |
| 2021 | Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation. Cunxiao Du, Zhaopeng Tu, Jing Jiang |
| 2021 | Out-of-Distribution Generalization via Risk Extrapolation (REx). David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville |
| 2021 | Outlier-Robust Optimal Transport. Debarghya Mukherjee, Aritra Guha, Justin M. Solomon, Yuekai Sun, Mikhail Yurochkin |
| 2021 | Outside the Echo Chamber: Optimizing the Performative Risk. John Miller, Juan C. Perdomo, Tijana Zrnic |
| 2021 | Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai |
| 2021 | PAC-Learning for Strategic Classification. Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao |
| 2021 | PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause |
| 2021 | PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. Zhize Li, Hongyan Bao, Xiangliang Zhang, Peter Richtárik |
| 2021 | PAPRIKA: Private Online False Discovery Rate Control. Wanrong Zhang, Gautam Kamath, Rachel Cummings |
| 2021 | PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. Yuda Song, Wen Sun |
| 2021 | PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training. Kimin Lee, Laura M. Smith, Pieter Abbeel |
| 2021 | PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data. Shreyas Malakarjun Patil, Constantine Dovrolis |
| 2021 | PID Accelerated Value Iteration Algorithm. Amir Massoud Farahmand, Mohammad Ghavamzadeh |
| 2021 | PODS: Policy Optimization via Differentiable Simulation. Miguel Zamora, Momchil Peychev, Sehoon Ha, Martin T. Vechev, Stelian Coros |
| 2021 | Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning. Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty, Cy Lee |
| 2021 | Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics. Vivek Jayaram, John Thickstun |
| 2021 | Parallel tempering on optimized paths. Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté |
| 2021 | Parallelizing Legendre Memory Unit Training. Narsimha Reddy Chilkuri, Chris Eliasmith |
| 2021 | Parameter-free Locally Accelerated Conditional Gradients. Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta |
| 2021 | Parameterless Transductive Feature Re-representation for Few-Shot Learning. Wentao Cui, Yuhong Guo |
| 2021 | Parametric Graph for Unimodal Ranking Bandit. Camille-Sovanneary Gauthier, Romaric Gaudel, Élisa Fromont, Boammani Aser Lompo |
| 2021 | Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions. Todd Huster, Jeremy E. J. Cohen, Zinan Lin, Kevin Chan, Charles A. Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar |
| 2021 | Partially Observed Exchangeable Modeling. Yang Li, Junier Oliva |
| 2021 | Path Planning using Neural A* Search. Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki |
| 2021 | Perceiver: General Perception with Iterative Attention. Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, João Carreira |
| 2021 | Permutation Weighting. David Arbour, Drew Dimmery, Arjun Sondhi |
| 2021 | Personalized Federated Learning using Hypernetworks. Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik |
| 2021 | Phase Transitions, Distance Functions, and Implicit Neural Representations. Yaron Lipman |
| 2021 | Phasic Policy Gradient. Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman |
| 2021 | PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models. Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr |
| 2021 | PixelTransformer: Sample Conditioned Signal Generation. Shubham Tulsiani, Abhinav Gupta |
| 2021 | Pointwise Binary Classification with Pairwise Confidence Comparisons. Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama |
| 2021 | Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks. Xuhui Fan, Bin Li, Yaqiong Li, Scott A. Sisson |
| 2021 | Policy Analysis using Synthetic Controls in Continuous-Time. Alexis Bellot, Mihaela van der Schaar |
| 2021 | Policy Caches with Successor Features. Mark W. Nemecek, Ron Parr |
| 2021 | Policy Gradient Bayesian Robust Optimization for Imitation Learning. Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg |
| 2021 | Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu |
| 2021 | Poolingformer: Long Document Modeling with Pooling Attention. Hang Zhang, Yeyun Gong, Yelong Shen, Weisheng Li, Jiancheng Lv, Nan Duan, Weizhu Chen |
| 2021 | PopSkipJump: Decision-Based Attack for Probabilistic Classifiers. Carl-Johann Simon-Gabriel, Noman Ahmed Sheikh, Andreas Krause |
| 2021 | Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama |
| 2021 | Post-selection inference with HSIC-Lasso. Tobias Freidling, Benjamin Poignard, Héctor Climente-González, Makoto Yamada |
| 2021 | Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods. Chris Nota, Philip S. Thomas, Bruno C. da Silva |
| 2021 | Practical and Private (Deep) Learning Without Sampling or Shuffling. Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu |
| 2021 | Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers. Yujia Bao, Shiyu Chang, Regina Barzilay |
| 2021 | Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations. Mateusz Wilinski, Andrey Y. Lokhov |
| 2021 | Preferential Temporal Difference Learning. Nishanth V. Anand, Doina Precup |
| 2021 | Principal Bit Analysis: Autoencoding with Schur-Concave Loss. Sourbh Bhadane, Aaron B. Wagner, Jayadev Acharya |
| 2021 | Principal Component Hierarchy for Sparse Quadratic Programs. Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani |
| 2021 | Principled Exploration via Optimistic Bootstrapping and Backward Induction. Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang |
| 2021 | Principled Simplicial Neural Networks for Trajectory Prediction. T. Mitchell Roddenberry, Nicholas Glaze, Santiago Segarra |
| 2021 | Prior Image-Constrained Reconstruction using Style-Based Generative Models. Varun A. Kelkar, Mark A. Anastasio |
| 2021 | Prioritized Level Replay. Minqi Jiang, Edward Grefenstette, Tim Rocktäschel |
| 2021 | Privacy-Preserving Feature Selection with Secure Multiparty Computation. Xiling Li, Rafael Dowsley, Martine De Cock |
| 2021 | Privacy-Preserving Video Classification with Convolutional Neural Networks. Sikha Pentyala, Rafael Dowsley, Martine De Cock |
| 2021 | Private Adaptive Gradient Methods for Convex Optimization. Hilal Asi, John C. Duchi, Alireza Fallah, Omid Javidbakht, Kunal Talwar |
| 2021 | Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang |
| 2021 | Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar |
| 2021 | ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O'Boyle, Hugh Leather |
| 2021 | Probabilistic Generating Circuits. Honghua Zhang, Brendan Juba, Guy Van den Broeck |
| 2021 | Probabilistic Programs with Stochastic Conditioning. David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang |
| 2021 | Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions. Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan |
| 2021 | Problem Dependent View on Structured Thresholding Bandit Problems. James Cheshire, Pierre Ménard, Alexandra Carpentier |
| 2021 | Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Marina Meila, Tong Zhang |
| 2021 | Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation. Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li |
| 2021 | Projection Robust Wasserstein Barycenters. Minhui Huang, Shiqian Ma, Lifeng Lai |
| 2021 | Projection techniques to update the truncated SVD of evolving matrices with applications. Vasileios Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth L. Clarkson |
| 2021 | Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. Spencer Frei, Yuan Cao, Quanquan Gu |
| 2021 | Provable Lipschitz Certification for Generative Models. Matt Jordan, Alex Dimakis |
| 2021 | Provable Meta-Learning of Linear Representations. Nilesh Tripuraneni, Chi Jin, Michael I. Jordan |
| 2021 | Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. Difan Zou, Spencer Frei, Quanquan Gu |
| 2021 | Provably Correct Optimization and Exploration with Non-linear Policies. Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang |
| 2021 | Provably Efficient Algorithms for Multi-Objective Competitive RL. Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra |
| 2021 | Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions. Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang |
| 2021 | Provably Efficient Learning of Transferable Rewards. Alberto Maria Metelli, Giorgia Ramponi, Alessandro Concetti, Marcello Restelli |
| 2021 | Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2021 | Provably End-to-end Label-noise Learning without Anchor Points. Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama |
| 2021 | Provably Strict Generalisation Benefit for Equivariant Models. Bryn Elesedy, Sheheryar Zaidi |
| 2021 | Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet |
| 2021 | PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar |
| 2021 | Pure Exploration and Regret Minimization in Matching Bandits. Flore Sentenac, Jialin Yi, Clément Calauzènes, Vianney Perchet, Milan Vojnovic |
| 2021 | Putting the "Learning" into Learning-Augmented Algorithms for Frequency Estimation. Elbert Du, Franklyn Wang, Michael Mitzenmacher |
| 2021 | Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability. Mihaela Curmei, Sarah Dean, Benjamin Recht |
| 2021 | Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit |
| 2021 | Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies. Uthsav Chitra, Kimberly Ding, Jasper C. H. Lee, Benjamin J. Raphael |
| 2021 | Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro |
| 2021 | Quantile Bandits for Best Arms Identification. Mengyan Zhang, Cheng Soon Ong |
| 2021 | Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding. Akira Nakagawa, Keizo Kato, Taiji Suzuki |
| 2021 | Quantization Algorithms for Random Fourier Features. Xiaoyun Li, Ping Li |
| 2021 | Quantum algorithms for reinforcement learning with a generative model. Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler |
| 2021 | Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi |
| 2021 | Query Complexity of Adversarial Attacks. Grzegorz Gluch, Rüdiger L. Urbanke |
| 2021 | RATT: Leveraging Unlabeled Data to Guarantee Generalization. Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton |
| 2021 | REPAINT: Knowledge Transfer in Deep Reinforcement Learning. Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya |
| 2021 | RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting. Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates |
| 2021 | RNNRepair: Automatic RNN Repair via Model-based Analysis. Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, Xinyu Xing |
| 2021 | RRL: Resnet as representation for Reinforcement Learning. Rutav M. Shah, Vikash Kumar |
| 2021 | Randomized Algorithms for Submodular Function Maximization with a k-System Constraint. Shuang Cui, Kai Han, Tianshuai Zhu, Jing Tang, Benwei Wu, He Huang |
| 2021 | Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering. Shyam Narayanan, Sandeep Silwal, Piotr Indyk, Or Zamir |
| 2021 | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha |
| 2021 | Randomized Exploration in Reinforcement Learning with General Value Function Approximation. Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang |
| 2021 | Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning. Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei, Mahdieh Soleymani Baghshah |
| 2021 | Re-understanding Finite-State Representations of Recurrent Policy Networks. Mohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram |
| 2021 | Reasoning Over Virtual Knowledge Bases With Open Predicate Relations. Haitian Sun, Patrick Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen |
| 2021 | Recomposing the Reinforcement Learning Building Blocks with Hypernetworks. Elad Sarafian, Shai Keynan, Sarit Kraus |
| 2021 | Recovering AES Keys with a Deep Cold Boot Attack. Itamar Zimerman, Eliya Nachmani, Lior Wolf |
| 2021 | Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach. Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher |
| 2021 | Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints. Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Henrik Johansson |
| 2021 | Regularized Online Allocation Problems: Fairness and Beyond. Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni |
| 2021 | Regularized Submodular Maximization at Scale. Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi |
| 2021 | Regularizing towards Causal Invariance: Linear Models with Proxies. Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag |
| 2021 | Reinforcement Learning Under Moral Uncertainty. Adrien Ecoffet, Joel Lehman |
| 2021 | Reinforcement Learning for Cost-Aware Markov Decision Processes. Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David N. Kraemer |
| 2021 | Reinforcement Learning of Implicit and Explicit Control Flow Instructions. Ethan Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh |
| 2021 | Reinforcement Learning with Prototypical Representations. Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto |
| 2021 | Relative Deviation Margin Bounds. Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh |
| 2021 | Relative Positional Encoding for Transformers with Linear Complexity. Antoine Liutkus, Ondrej Cífka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gaël Richard |
| 2021 | Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data. Esther Rolf, Theodora T. Worledge, Benjamin Recht, Michael I. Jordan |
| 2021 | Representation Matters: Offline Pretraining for Sequential Decision Making. Mengjiao Yang, Ofir Nachum |
| 2021 | Representation Subspace Distance for Domain Adaptation Regression. Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long |
| 2021 | Representational aspects of depth and conditioning in normalizing flows. Frederic Koehler, Viraj Mehta, Andrej Risteski |
| 2021 | Reserve Price Optimization for First Price Auctions in Display Advertising. Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye |
| 2021 | Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez |
| 2021 | Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives. Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan |
| 2021 | Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss. Xue Yang, Junchi Yan, Qi Ming, Wentao Wang, Xiaopeng Zhang, Qi Tian |
| 2021 | Revealing the Structure of Deep Neural Networks via Convex Duality. Tolga Ergen, Mert Pilanci |
| 2021 | Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing. Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni, Song Zuo |
| 2021 | Revisiting Peng's Q(λ) for Modern Reinforcement Learning. Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel |
| 2021 | Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline. Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng |
| 2021 | Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research. Johan S. Obando-Ceron, Pablo Samuel Castro |
| 2021 | Reward Identification in Inverse Reinforcement Learning. Kuno Kim, Shivam Garg, Kirankumar Shiragur, Stefano Ermon |
| 2021 | Riemannian Convex Potential Maps. Samuel Cohen, Brandon Amos, Yaron Lipman |
| 2021 | Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning. Yaqi Duan, Chi Jin, Zhiyuan Li |
| 2021 | Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach. Yingjie Fei, Zhuoran Yang, Zhaoran Wang |
| 2021 | Rissanen Data Analysis: Examining Dataset Characteristics via Description Length. Ethan Perez, Douwe Kiela, Kyunghyun Cho |
| 2021 | Robust Asymmetric Learning in POMDPs. Andrew Warrington, Jonathan Wilder Lavington, Adam Scibior, Mark Schmidt, Frank Wood |
| 2021 | Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free. Ayush Jain, Alon Orlitsky |
| 2021 | Robust Inference for High-Dimensional Linear Models via Residual Randomization. Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar |
| 2021 | Robust Learning for Data Poisoning Attacks. Yunjuan Wang, Poorya Mianjy, Raman Arora |
| 2021 | Robust Learning-Augmented Caching: An Experimental Study. Jakub Chledowski, Adam Polak, Bartosz Szabucki, Konrad Tomasz Zolna |
| 2021 | Robust Policy Gradient against Strong Data Corruption. Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun |
| 2021 | Robust Pure Exploration in Linear Bandits with Limited Budget. Ayya Alieva, Ashok Cutkosky, Abhimanyu Das |
| 2021 | Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees. Kishan Panaganti Badrinath, Dileep Kalathil |
| 2021 | Robust Representation Learning via Perceptual Similarity Metrics. Saeid Asgari Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, Anirudh Goyal |
| 2021 | Robust Testing and Estimation under Manipulation Attacks. Jayadev Acharya, Ziteng Sun, Huanyu Zhang |
| 2021 | Robust Unsupervised Learning via L-statistic Minimization. Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil |
| 2021 | Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models. José Lezama, Wei Chen, Qiang Qiu |
| 2021 | SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning. Lokesh Chandra Das, Myounggyu Won |
| 2021 | SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II. Xiangjun Wang, Junxiao Song, Penghui Qi, Peng Peng, Zhenkun Tang, Wei Zhang, Weimin Li, Xiongjun Pi, Jujie He, Chao Gao, Haitao Long, Quan Yuan |
| 2021 | SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies. Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Animashree Anandkumar |
| 2021 | SG-PALM: a Fast Physically Interpretable Tensor Graphical Model. Yu Wang, Alfred Olivier Hero |
| 2021 | SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples. Anshoo Tandon, Aldric H. J. Han, Vincent Y. F. Tan |
| 2021 | SGLB: Stochastic Gradient Langevin Boosting. Aleksei Ustimenko, Liudmila Prokhorenkova |
| 2021 | SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson |
| 2021 | SMG: A Shuffling Gradient-Based Method with Momentum. Trang H. Tran, Lam M. Nguyen, Quoc Tran-Dinh |
| 2021 | SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation. Wuxinlin Cheng, Chenhui Deng, Zhiqiang Zhao, Yaohui Cai, Zhiru Zhang, Zhuo Feng |
| 2021 | STRODE: Stochastic Boundary Ordinary Differential Equation. Hengguan Huang, Hongfu Liu, Hao Wang, Chang Xiao, Ye Wang |
| 2021 | SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning. Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel |
| 2021 | Safe Reinforcement Learning Using Advantage-Based Intervention. Nolan Wagener, Byron Boots, Ching-An Cheng |
| 2021 | Safe Reinforcement Learning with Linear Function Approximation. Sanae Amani, Christos Thrampoulidis, Lin Yang |
| 2021 | SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis. Yuhan Liu, Shiliang Sun |
| 2021 | Sample Complexity of Robust Linear Classification on Separated Data. Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri |
| 2021 | Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li |
| 2021 | Sample-Optimal PAC Learning of Halfspaces with Malicious Noise. Jie Shen |
| 2021 | Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network. Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou |
| 2021 | Scalable Certified Segmentation via Randomized Smoothing. Marc Fischer, Maximilian Baader, Martin T. Vechev |
| 2021 | Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks. Yongxin Chen, Jiaojiao Fan, Amirhossein Taghvaei |
| 2021 | Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel |
| 2021 | Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan |
| 2021 | Scalable Normalizing Flows for Permutation Invariant Densities. Marin Bilos, Stephan Günnemann |
| 2021 | Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. Johannes Klicpera, Marten Lienen, Stephan Günnemann |
| 2021 | Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse |
| 2021 | Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing. Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht |
| 2021 | Scaling Properties of Deep Residual Networks. Alain-Sam Cohen, Rama Cont, Alain Rossier, Renyuan Xu |
| 2021 | Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, Tom Duerig |
| 2021 | Segmenting Hybrid Trajectories using Latent ODEs. Ruian Shi, Quaid Morris |
| 2021 | Selecting Data Augmentation for Simulating Interventions. Maximilian Ilse, Jakub M. Tomczak, Patrick Forré |
| 2021 | Self Normalizing Flows. T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling |
| 2021 | Self-Damaging Contrastive Learning. Ziyu Jiang, Tianlong Chen, Bobak J. Mortazavi, Zhangyang Wang |
| 2021 | Self-Improved Retrosynthetic Planning. Junsu Kim, Sungsoo Ahn, Hankook Lee, Jinwoo Shin |
| 2021 | Self-Paced Context Evaluation for Contextual Reinforcement Learning. Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer |
| 2021 | Self-Tuning for Data-Efficient Deep Learning. Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang |
| 2021 | Self-supervised Graph-level Representation Learning with Local and Global Structure. Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang |
| 2021 | Self-supervised and Supervised Joint Training for Resource-rich Machine Translation. Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey |
| 2021 | Selfish Sparse RNN Training. Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy |
| 2021 | Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts. Bahar Taskesen, Man-Chung Yue, Jose H. Blanchet, Daniel Kuhn, Viet Anh Nguyen |
| 2021 | Sharf: Shape-conditioned Radiance Fields from a Single View. Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari |
| 2021 | Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer. Seungwon Lee, Sima Behpour, Eric Eaton |
| 2021 | Sharper Generalization Bounds for Clustering. Shaojie Li, Yong Liu |
| 2021 | Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks. Sungryull Sohn, Sungtae Lee, Jongwook Choi, Harm van Seijen, Mehdi Fatemi, Honglak Lee |
| 2021 | SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels. Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao, Amit Singh, Sumeet Agarwal, Purushottam Kar, Manik Varma |
| 2021 | SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data. Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry J. Lyons |
| 2021 | Signatured Deep Fictitious Play for Mean Field Games with Common Noise. Ming Min, Ruimeng Hu |
| 2021 | SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Lingxiao Yang, Ru-Yuan Zhang, Lida Li, Xiaohua Xie |
| 2021 | Simple and Effective VAE Training with Calibrated Decoders. Oleh Rybkin, Kostas Daniilidis, Sergey Levine |
| 2021 | Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi |
| 2021 | SinIR: Efficient General Image Manipulation with Single Image Reconstruction. Jihyeong Yoo, Qifeng Chen |
| 2021 | Single Pass Entrywise-Transformed Low Rank Approximation. Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David P. Woodruff |
| 2021 | Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. Kai Sheng Tai, Peter Bailis, Gregory Valiant |
| 2021 | Size-Invariant Graph Representations for Graph Classification Extrapolations. Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro |
| 2021 | SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. Alexander Wang, Mengye Ren, Richard S. Zemel |
| 2021 | Skew Orthogonal Convolutions. Sahil Singla, Soheil Feizi |
| 2021 | Skill Discovery for Exploration and Planning using Deep Skill Graphs. Akhil Bagaria, Jason K. Senthil, George Konidaris |
| 2021 | Sliced Iterative Normalizing Flows. Biwei Dai, Uros Seljak |
| 2021 | Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks. Maxwell Mbabilla Aladago, Lorenzo Torresani |
| 2021 | Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications. Sloan Nietert, Ziv Goldfeld, Kengo Kato |
| 2021 | Soft then Hard: Rethinking the Quantization in Neural Image Compression. Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen |
| 2021 | Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning. Henry Charlesworth, Giovanni Montana |
| 2021 | Solving Inverse Problems with a Flow-based Noise Model. Jay Whang, Qi Lei, Alex Dimakis |
| 2021 | Solving high-dimensional parabolic PDEs using the tensor train format. Lorenz Richter, Leon Sallandt, Nikolas Nüsken |
| 2021 | SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform. Yuhang He, Niki Trigoni, Andrew Markham |
| 2021 | Sparse Bayesian Learning via Stepwise Regression. Sebastian E. Ament, Carla P. Gomes |
| 2021 | Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang |
| 2021 | Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm. Mingkang Zhu, Tianlong Chen, Zhangyang Wang |
| 2021 | Sparse within Sparse Gaussian Processes using Neighbor Information. Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone |
| 2021 | SparseBERT: Rethinking the Importance Analysis in Self-attention. Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James Tin-Yau Kwok |
| 2021 | Sparsifying Networks via Subdifferential Inclusion. Sagar Verma, Jean-Christophe Pesquet |
| 2021 | Sparsity-Agnostic Lasso Bandit. Min-hwan Oh, Garud Iyengar, Assaf Zeevi |
| 2021 | Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective. Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu |
| 2021 | Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders. Adeel Pervez, Efstratios Gavves |
| 2021 | Spectral vertex sparsifiers and pair-wise spanners over distributed graphs. Chunjiang Zhu, Qinqing Liu, Jinbo Bi |
| 2021 | SpreadsheetCoder: Formula Prediction from Semi-structured Context. Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou |
| 2021 | Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness. Vien V. Mai, Mikael Johansson |
| 2021 | Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying |
| 2021 | Stabilizing Equilibrium Models by Jacobian Regularization. Shaojie Bai, Vladlen Koltun, J. Zico Kolter |
| 2021 | State Entropy Maximization with Random Encoders for Efficient Exploration. Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee |
| 2021 | State Relevance for Off-Policy Evaluation. Simon P. Shen, Yecheng Jason Ma, Omer Gottesman, Finale Doshi-Velez |
| 2021 | Statistical Estimation from Dependent Data. Anthimos Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis |
| 2021 | Stochastic Iterative Graph Matching. Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu |
| 2021 | Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions. Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour |
| 2021 | Stochastic Sign Descent Methods: New Algorithms and Better Theory. Mher Safaryan, Peter Richtárik |
| 2021 | Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation. Xiang Lin, Simeng Han, Shafiq R. Joty |
| 2021 | Strategic Classification Made Practical. Sagi Levanon, Nir Rosenfeld |
| 2021 | Strategic Classification in the Dark. Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld |
| 2021 | Streaming Bayesian Deep Tensor Factorization. Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe |
| 2021 | Streaming and Distributed Algorithms for Robust Column Subset Selection. Shuli Jiang, Dennis Li, Irene Mengze Li, Arvind V. Mahankali, David P. Woodruff |
| 2021 | Structured Convolutional Kernel Networks for Airline Crew Scheduling. Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien |
| 2021 | Structured World Belief for Reinforcement Learning in POMDP. Gautam Singh, Skand Vishwanath Peri, Junghyun Kim, Hyunseok Kim, Sungjin Ahn |
| 2021 | Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity. Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti-Spaccamela, Rebecca Reiffenhäuser |
| 2021 | Supervised Tree-Wasserstein Distance. Yuki Takezawa, Ryoma Sato, Makoto Yamada |
| 2021 | Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach. Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard |
| 2021 | Synthesizer: Rethinking Self-Attention for Transformer Models. Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng |
| 2021 | Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures. Martijn Gösgens, Alexey Tikhonov, Liudmila Prokhorenkova |
| 2021 | T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP. Jiaye Teng, Zeren Tan, Yang Yuan |
| 2021 | TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer. Berkay Berabi, Jingxuan He, Veselin Raychev, Martin T. Vechev |
| 2021 | Targeted Data Acquisition for Evolving Negotiation Agents. Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuellar, Dorsa Sadigh |
| 2021 | Task-Optimal Exploration in Linear Dynamical Systems. Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson |
| 2021 | Taylor Expansion of Discount Factors. Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko |
| 2021 | TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL. Clément Romac, Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer |
| 2021 | TempoRL: Learning When to Act. André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer |
| 2021 | Temporal Difference Learning as Gradient Splitting. Rui Liu, Alex Olshevsky |
| 2021 | Temporal Predictive Coding For Model-Based Planning In Latent Space. Tung D. Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon |
| 2021 | Temporally Correlated Task Scheduling for Sequence Learning. Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu |
| 2021 | Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics. Greg Yang, Etai Littwin |
| 2021 | Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks. Greg Yang, Edward J. Hu |
| 2021 | TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models. Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, Ion Stoica |
| 2021 | Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning. Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar |
| 2021 | Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions. Zhong Li, Minxue Pan, Tian Zhang, Xuandong Li |
| 2021 | Testing Group Fairness via Optimal Transport Projections. Nian Si, Karthyek Murthy, Jose H. Blanchet, Viet Anh Nguyen |
| 2021 | The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. Peter Kairouz, Ziyu Liu, Thomas Steinke |
| 2021 | The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression. Florian List |
| 2021 | The Emergence of Individuality. Jiechuan Jiang, Zongqing Lu |
| 2021 | The Heavy-Tail Phenomenon in SGD. Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu |
| 2021 | The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning. Roberto Bondesan, Max Welling |
| 2021 | The Impact of Record Linkage on Learning from Feature Partitioned Data. Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne |
| 2021 | The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks. Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu |
| 2021 | The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets. Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher |
| 2021 | The Lipschitz Constant of Self-Attention. Hyunjik Kim, George Papamakarios, Andriy Mnih |
| 2021 | The Logical Options Framework. Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan A. DeCastro, Micah J. Fry, Daniela Rus |
| 2021 | The Power of Adaptivity for Stochastic Submodular Cover. Rohan Ghuge, Anupam Gupta, Viswanath Nagarajan |
| 2021 | The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization. Taiki Miyagawa, Akinori F. Ebihara |
| 2021 | The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks. Xiaocheng Li, Chunlin Sun, Yinyu Ye |
| 2021 | Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph. Gen Li, Yuantao Gu |
| 2021 | Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. Xingchen Wan, Vu Nguyen, Huong Ha, Bin Xin Ru, Cong Lu, Michael A. Osborne |
| 2021 | Thinking Like Transformers. Gail Weiss, Yoav Goldberg, Eran Yahav |
| 2021 | Three Operator Splitting with a Nonconvex Loss Function. Alp Yurtsever, Varun Mangalick, Suvrit Sra |
| 2021 | Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. Quynh Nguyen, Marco Mondelli, Guido F. Montúfar |
| 2021 | Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning. Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi |
| 2021 | Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. Artem Artemev, David R. Burt, Mark van der Wilk |
| 2021 | Tilting the playing field: Dynamical loss functions for machine learning. Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu |
| 2021 | To be Robust or to be Fair: Towards Fairness in Adversarial Training. Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang |
| 2021 | Top-k eXtreme Contextual Bandits with Arm Hierarchy. Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon |
| 2021 | Toward Better Generalization Bounds with Locally Elastic Stability. Zhun Deng, Hangfeng He, Weijie J. Su |
| 2021 | Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning. Zixin Wen, Yuanzhi Li |
| 2021 | Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing. Kaixin Wang, Kuangqi Zhou, Qixin Zhang, Jie Shao, Bryan Hooi, Jiashi Feng |
| 2021 | Towards Better Robust Generalization with Shift Consistency Regularization. Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi |
| 2021 | Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons. Bohang Zhang, Tianle Cai, Zhou Lu, Di He, Liwei Wang |
| 2021 | Towards Defending against Adversarial Examples via Attack-Invariant Features. Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao |
| 2021 | Towards Distraction-Robust Active Visual Tracking. Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang |
| 2021 | Towards Domain-Agnostic Contrastive Learning. Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le |
| 2021 | Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning. Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht |
| 2021 | Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach. Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha |
| 2021 | Towards Practical Mean Bounds for Small Samples. My Phan, Philip S. Thomas, Erik G. Learned-Miller |
| 2021 | Towards Rigorous Interpretations: a Formalisation of Feature Attribution. Darius Afchar, Vincent Guigue, Romain Hennequin |
| 2021 | Towards Tight Bounds on the Sample Complexity of Average-reward MDPs. Yujia Jin, Aaron Sidford |
| 2021 | Towards Understanding Learning in Neural Networks with Linear Teachers. Roei Sarussi, Alon Brutzkus, Amir Globerson |
| 2021 | Towards Understanding and Mitigating Social Biases in Language Models. Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, Ruslan Salakhutdinov |
| 2021 | Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju |
| 2021 | Tractable structured natural-gradient descent using local parameterizations. Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2021 | Train simultaneously, generalize better: Stability of gradient-based minimax learners. Farzan Farnia, Asuman E. Ozdaglar |
| 2021 | Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling. Ozan Özdenizci, Robert Legenstein |
| 2021 | Training Data Subset Selection for Regression with Controlled Generalization Error. Durga Sivasubramanian, Rishabh K. Iyer, Ganesh Ramakrishnan, Abir De |
| 2021 | Training Graph Neural Networks with 1000 Layers. Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun |
| 2021 | Training Quantized Neural Networks to Global Optimality via Semidefinite Programming. Burak Bartan, Mert Pilanci |
| 2021 | Training Recurrent Neural Networks via Forward Propagation Through Time. Anil Kag, Venkatesh Saligrama |
| 2021 | Training data-efficient image transformers & distillation through attention. Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou |
| 2021 | Trajectory Diversity for Zero-Shot Coordination. Andrei Lupu, Brandon Cui, Hengyuan Hu, Jakob N. Foerster |
| 2021 | Transfer-Based Semantic Anomaly Detection. Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen |
| 2021 | Trees with Attention for Set Prediction Tasks. Roy Hirsch, Ran Gilad-Bachrach |
| 2021 | Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination. Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang |
| 2021 | Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering. Romain Couillet, Florent Chatelain, Nicolas Le Bihan |
| 2021 | UCB Momentum Q-learning: Correcting the bias without forgetting. Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko |
| 2021 | UnICORNN: A recurrent model for learning very long time dependencies. T. Konstantin Rusch, Siddhartha Mishra |
| 2021 | Unbalanced minibatch Optimal Transport; applications to Domain Adaptation. Kilian Fatras, Thibault Séjourné, Rémi Flamary, Nicolas Courty |
| 2021 | Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies. Paul Vicol, Luke Metz, Jascha Sohl-Dickstein |
| 2021 | Uncertainty Principles of Encoding GANs. Ruili Feng, Zhouchen Lin, Jiapeng Zhu, Deli Zhao, Jingren Zhou, Zheng-Jun Zha |
| 2021 | Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning. Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh |
| 2021 | Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability. Kaizhao Liang, Jacky Y. Zhang, Boxin Wang, Zhuolin Yang, Sanmi Koyejo, Bo Li |
| 2021 | Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. Lily H. Zhang, Mark Goldstein, Rajesh Ranganath |
| 2021 | Understanding Instance-Level Label Noise: Disparate Impacts and Treatments. Yang Liu |
| 2021 | Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers. Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer |
| 2021 | Understanding Noise Injection in GANs. Ruili Feng, Deli Zhao, Zheng-Jun Zha |
| 2021 | Understanding and Mitigating Accuracy Disparity in Regression. Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao |
| 2021 | Understanding self-supervised learning dynamics without contrastive pairs. Yuandong Tian, Xinlei Chen, Surya Ganguli |
| 2021 | Understanding the Dynamics of Gradient Flow in Overparameterized Linear models. Salma Tarmoun, Guilherme França, Benjamin D. Haeffele, René Vidal |
| 2021 | UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson |
| 2021 | UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data. Chengyi Wang, Yu Wu, Yao Qian, Ken'ichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang |
| 2021 | Unified Robust Semi-Supervised Variational Autoencoder. Xu Chen |
| 2021 | Uniform Convergence, Adversarial Spheres and a Simple Remedy. Gregor Bachmann, Seyed-Mohsen Moosavi-Dezfooli, Thomas Hofmann |
| 2021 | Unifying Vision-and-Language Tasks via Text Generation. Jaemin Cho, Jie Lei, Hao Tan, Mohit Bansal |
| 2021 | Unitary Branching Programs: Learnability and Lower Bounds. Fidel Ernesto Diaz Andino, Maria Kokkou, Mateus de Oliveira Oliveira, Farhad Vadiee |
| 2021 | Unsupervised Co-part Segmentation through Assembly. Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen |
| 2021 | Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification. Dong Hoon Lee, Sae-Young Chung |
| 2021 | Unsupervised Learning of Visual 3D Keypoints for Control. Boyuan Chen, Pieter Abbeel, Deepak Pathak |
| 2021 | Unsupervised Part Representation by Flow Capsules. Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet |
| 2021 | Unsupervised Representation Learning via Neural Activation Coding. Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei |
| 2021 | Unsupervised Skill Discovery with Bottleneck Option Learning. Jaekyeom Kim, Seohong Park, Gunhee Kim |
| 2021 | Valid Causal Inference with (Some) Invalid Instruments. Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown |
| 2021 | Value Alignment Verification. Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum |
| 2021 | Value Iteration in Continuous Actions, States and Time. Michael Lutter, Shie Mannor, Jan Peters, Dieter Fox, Animesh Garg |
| 2021 | Value-at-Risk Optimization with Gaussian Processes. Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2021 | Variance Reduced Training with Stratified Sampling for Forecasting Models. Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster |
| 2021 | Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums. Chaobing Song, Stephen J. Wright, Jelena Diakonikolas |
| 2021 | Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang |
| 2021 | Variational Auto-Regressive Gaussian Processes for Continual Learning. Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui |
| 2021 | Variational Data Assimilation with a Learned Inverse Observation Operator. Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer |
| 2021 | Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning. Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, Shixiang Shane Gu |
| 2021 | Vector Quantized Models for Planning. Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals |
| 2021 | Versatile Verification of Tree Ensembles. Laurens Devos, Wannes Meert, Jesse Davis |
| 2021 | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision. Wonjae Kim, Bokyung Son, Ildoo Kim |
| 2021 | Voice2Series: Reprogramming Acoustic Models for Time Series Classification. Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen |
| 2021 | WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points. Albert No, Taeho Yoon, Sehyun Kwon, Ernest K. Ryu |
| 2021 | WILDS: A Benchmark of in-the-Wild Distribution Shifts. Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran S. Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang |
| 2021 | Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data. Sung Woo Park, Junseok Kwon |
| 2021 | Watermarking Deep Neural Networks with Greedy Residuals. Hanwen Liu, Zhenyu Weng, Yuesheng Zhu |
| 2021 | Weight-covariance alignment for adversarially robust neural networks. Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy M. Hospedales |
| 2021 | Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F. Montúfar, Pietro Lió, Michael M. Bronstein |
| 2021 | What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson |
| 2021 | What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? Weijian Deng, Stephen Gould, Liang Zheng |
| 2021 | What Makes for End-to-End Object Detection? Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan, Changhu Wang, Ping Luo |
| 2021 | What does LIME really see in images? Damien Garreau, Dina Mardaoui |
| 2021 | What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. Jonas Fischer, Anna Oláh, Jilles Vreeken |
| 2021 | When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC. Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang |
| 2021 | When Does Data Augmentation Help With Membership Inference Attacks? Yigitcan Kaya, Tudor Dumitras |
| 2021 | Which transformer architecture fits my data? A vocabulary bottleneck in self-attention. Noam Wies, Yoav Levine, Daniel Jannai, Amnon Shashua |
| 2021 | Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization. Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein |
| 2021 | Whitening for Self-Supervised Representation Learning. Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe |
| 2021 | Whittle Networks: A Deep Likelihood Model for Time Series. Zhongjie Yu, Fabrizio Ventola, Kristian Kersting |
| 2021 | Winograd Algorithm for AdderNet. Wenshuo Li, Hanting Chen, Mingqiang Huang, Xinghao Chen, Chunjing Xu, Yunhe Wang |
| 2021 | World Model as a Graph: Learning Latent Landmarks for Planning. Lunjun Zhang, Ge Yang, Bradly C. Stadie |
| 2021 | XOR-CD: Linearly Convergent Constrained Structure Generation. Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue |
| 2021 | You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling. Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Moo Fung, Vikas Singh |
| 2021 | Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting. Yuzhou Chen, Ignacio Segovia-Dominguez, Yulia R. Gel |
| 2021 | Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model. Zi Wang |
| 2021 | Zero-Shot Text-to-Image Generation. Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever |
| 2021 | Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging. Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier |
| 2021 | Zoo-Tuning: Adaptive Transfer from A Zoo of Models. Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long |
| 2021 | f-Domain Adversarial Learning: Theory and Algorithms. David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler |
| 2021 | iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, M. Ehsan Abbasnejad, Reza Haffari |