| 2020 | (De)Randomized Smoothing for Certifiable Defense against Patch Attacks. Alexander Levine, Soheil Feizi |
| 2020 | 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data. Benjamin Biggs, David Novotný, Sébastien Ehrhardt, Hanbyul Joo, Benjamin Graham, Andrea Vedaldi |
| 2020 | 3D Self-Supervised Methods for Medical Imaging. Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gärtner, Benjamin Bergner, Christoph Lippert |
| 2020 | 3D Shape Reconstruction from Vision and Touch. Edward J. Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal |
| 2020 | A Bandit Learning Algorithm and Applications to Auction Design. Kim Thang Nguyen |
| 2020 | A Bayesian Nonparametrics View into Deep Representations. Michal Jamroz, Marcin Kurdziel, Mateusz Opala |
| 2020 | A Bayesian Perspective on Training Speed and Model Selection. Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk |
| 2020 | A Benchmark for Systematic Generalization in Grounded Language Understanding. Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake |
| 2020 | A Biologically Plausible Neural Network for Slow Feature Analysis. David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri B. Chklovskii |
| 2020 | A Boolean Task Algebra for Reinforcement Learning. Geraud Nangue Tasse, Steven James, Benjamin Rosman |
| 2020 | A Catalyst Framework for Minimax Optimization. Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He |
| 2020 | A Causal View on Robustness of Neural Networks. Cheng Zhang, Kun Zhang, Yingzhen Li |
| 2020 | A Class of Algorithms for General Instrumental Variable Models. Niki Kilbertus, Matt J. Kusner, Ricardo Silva |
| 2020 | A Closer Look at Accuracy vs. Robustness. Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri |
| 2020 | A Closer Look at the Training Strategy for Modern Meta-Learning. Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, Fu-Lai Chung |
| 2020 | A Combinatorial Perspective on Transfer Learning. Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness |
| 2020 | A Computational Separation between Private Learning and Online Learning. Mark Bun |
| 2020 | A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval. Fan Wu, Patrick Rebeschini |
| 2020 | A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. Wei Deng, Guang Lin, Faming Liang |
| 2020 | A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction. Ziqi Ke, Haris Vikalo |
| 2020 | A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das |
| 2020 | A Dictionary Approach to Domain-Invariant Learning in Deep Networks. Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu |
| 2020 | A Discrete Variational Recurrent Topic Model without the Reparametrization Trick. Mehdi Rezaee, Francis Ferraro |
| 2020 | A Dynamical Central Limit Theorem for Shallow Neural Networks. Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden |
| 2020 | A Fair Classifier Using Kernel Density Estimation. Jaewoong Cho, Gyeongjo Hwang, Changho Suh |
| 2020 | A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization. Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang |
| 2020 | A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu |
| 2020 | A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding. Bruno Lecouat, Jean Ponce, Julien Mairal |
| 2020 | A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses. Ambar Pal, René Vidal |
| 2020 | A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling. Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei Wang, Xiang Yan |
| 2020 | A General Large Neighborhood Search Framework for Solving Integer Linear Programs. Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina |
| 2020 | A General Method for Robust Learning from Batches. Ayush Jain, Alon Orlitsky |
| 2020 | A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang |
| 2020 | A Group-Theoretic Framework for Data Augmentation. Shuxiao Chen, Edgar Dobriban, Jane H. Lee |
| 2020 | A Limitation of the PAC-Bayes Framework. Roi Livni, Shay Moran |
| 2020 | A Local Temporal Difference Code for Distributional Reinforcement Learning. Pablo Tano, Peter Dayan, Alexandre Pouget |
| 2020 | A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model. Steffen Czolbe, Oswin Krause, Ingemar J. Cox, Christian Igel |
| 2020 | A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices. Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang |
| 2020 | A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs. Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans |
| 2020 | A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. Junhyung Park, Krikamol Muandet |
| 2020 | A Non-Asymptotic Analysis for Stein Variational Gradient Descent. Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton |
| 2020 | A Novel Approach for Constrained Optimization in Graphical Models. Sara Rouhani, Tahrima Rahman, Vibhav Gogate |
| 2020 | A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances. Dieqiao Feng, Carla P. Gomes, Bart Selman |
| 2020 | A Randomized Algorithm to Reduce the Support of Discrete Measures. Francesco Cosentino, Harald Oberhauser, Alessandro Abate |
| 2020 | A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection. Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan |
| 2020 | A Robust Functional EM Algorithm for Incomplete Panel Count Data. Alexander Moreno, Zhenke Wu, Jamie Yap, Cho Lam, David W. Wetter, Inbal Nahum-Shani, Walter H. Dempsey, James M. Rehg |
| 2020 | A Scalable Approach for Privacy-Preserving Collaborative Machine Learning. Jinhyun So, Basak Güler, Salman Avestimehr |
| 2020 | A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees. Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant Kalagnanam |
| 2020 | A Self-Tuning Actor-Critic Algorithm. Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh |
| 2020 | A Simple Language Model for Task-Oriented Dialogue. Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher |
| 2020 | A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin |
| 2020 | A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints. Omid Sadeghi, Prasanna Sanjay Raut, Maryam Fazel |
| 2020 | A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems. Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhi-Quan Luo |
| 2020 | A Spectral Energy Distance for Parallel Speech Synthesis. Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner |
| 2020 | A Statistical Framework for Low-bitwidth Training of Deep Neural Networks. Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez |
| 2020 | A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer |
| 2020 | A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm. Gersende Fort, Eric Moulines, Hoi-To Wai |
| 2020 | A Study on Encodings for Neural Architecture Search. Colin White, Willie Neiswanger, Sam Nolen, Yash Savani |
| 2020 | A Theoretical Framework for Target Propagation. Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe |
| 2020 | A Tight Lower Bound and Efficient Reduction for Swap Regret. Shinji Ito |
| 2020 | A Topological Filter for Learning with Label Noise. Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris N. Metaxas, Chao Chen |
| 2020 | A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. Donghwan Lee, Niao He |
| 2020 | A Unified View of Label Shift Estimation. Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton |
| 2020 | A Unifying View of Optimism in Episodic Reinforcement Learning. Gergely Neu, Ciara Pike-Burke |
| 2020 | A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions. Yulong Lu, Jianfeng Lu |
| 2020 | A Variational Approach for Learning from Positive and Unlabeled Data. Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu |
| 2020 | A causal view of compositional zero-shot recognition. Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik |
| 2020 | A convex optimization formulation for multivariate regression. Yunzhang Zhu |
| 2020 | A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning. Arnu Pretorius, Scott Alexander Cameron, Elan Van Biljon, Tom Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan Shock, Alexandre Laterre, Karim Beguir |
| 2020 | A graph similarity for deep learning. Seongmin Ok |
| 2020 | A kernel test for quasi-independence. Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton |
| 2020 | A mathematical model for automatic differentiation in machine learning. Jérôme Bolte, Edouard Pauwels |
| 2020 | A mathematical theory of cooperative communication. Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto |
| 2020 | A mean-field analysis of two-player zero-sum games. Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna |
| 2020 | A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels |
| 2020 | A new convergent variant of Q-learning with linear function approximation. Diogo S. Carvalho, Francisco S. Melo, Pedro Santos |
| 2020 | A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons. Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora |
| 2020 | A novel variational form of the Schatten-$p$ quasi-norm. Paris Giampouras, René Vidal, Athanasios A. Rontogiannis, Benjamin D. Haeffele |
| 2020 | A polynomial-time algorithm for learning nonparametric causal graphs. Ming Gao, Yi Ding, Bryon Aragam |
| 2020 | A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent. Zhenyu Liao, Romain Couillet, Michael W. Mahoney |
| 2020 | A shooting formulation of deep learning. François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer |
| 2020 | A simple normative network approximates local non-Hebbian learning in the cortex. Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii |
| 2020 | A/B Testing in Dense Large-Scale Networks: Design and Inference. Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu |
| 2020 | AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark |
| 2020 | AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection. Hao Zhu, Chaoyou Fu, Qianyi Wu, Wayne Wu, Chen Qian, Ran He |
| 2020 | ARMA Nets: Expanding Receptive Field for Dense Prediction. Jiahao Su, Shiqi Wang, Furong Huang |
| 2020 | AViD Dataset: Anonymized Videos from Diverse Countries. A. J. Piergiovanni, Michael S. Ryoo |
| 2020 | Accelerating Reinforcement Learning through GPU Atari Emulation. Steven Dalton, Iuri Frosio |
| 2020 | Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. Minjia Zhang, Yuxiong He |
| 2020 | Acceleration with a Ball Optimization Oracle. Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian |
| 2020 | Achieving Equalized Odds by Resampling Sensitive Attributes. Yaniv Romano, Stephen Bates, Emmanuel J. Candès |
| 2020 | Active Invariant Causal Prediction: Experiment Selection through Stability. Juan L. Gamella, Christina Heinze-Deml |
| 2020 | Active Structure Learning of Causal DAGs via Directed Clique Trees. Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam |
| 2020 | AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients. Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha C. Dvornek, Xenophon Papademetris, James S. Duncan |
| 2020 | AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning. Ximeng Sun, Rameswar Panda, Rogério Feris, Kate Saenko |
| 2020 | AdaTune: Adaptive Tensor Program Compilation Made Efficient. Menghao Li, Minjia Zhang, Chi Wang, Mingqin Li |
| 2020 | Adam with Bandit Sampling for Deep Learning. Rui Liu, Tianyi Wu, Barzan Mozafari |
| 2020 | Adaptation Properties Allow Identification of Optimized Neural Codes. Luke I. Rast, Jan Drugowitsch |
| 2020 | Adapting Neural Architectures Between Domains. Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu |
| 2020 | Adapting to Misspecification in Contextual Bandits. Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert |
| 2020 | Adaptive Discretization for Model-Based Reinforcement Learning. Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Lee Yu |
| 2020 | Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach. Peter W. Glynn, Ramesh Johari, Mohammad Rasouli |
| 2020 | Adaptive Gradient Quantization for Data-Parallel SGD. Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya |
| 2020 | Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang |
| 2020 | Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes. Ayoub El Hanchi, David A. Stephens |
| 2020 | Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web. Zhenwei Dai, Anshumali Shrivastava |
| 2020 | Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment. Govinda M. Kamath, Tavor Z. Baharav, Ilan Shomorony |
| 2020 | Adaptive Online Estimation of Piecewise Polynomial Trends. Dheeraj Baby, Yu-Xiang Wang |
| 2020 | Adaptive Probing Policies for Shortest Path Routing. Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala |
| 2020 | Adaptive Reduced Rank Regression. Qiong Wu, Felix Ming Fai Wong, Yanhua Li, Zhenming Liu, Varun Kanade |
| 2020 | Adaptive Sampling for Stochastic Risk-Averse Learning. Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause |
| 2020 | Adaptive Shrinkage Estimation for Streaming Graphs. Nesreen K. Ahmed, Nick Duffield |
| 2020 | AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows. Hadi Mohaghegh Dolatabadi, Sarah M. Erfani, Christopher Leckie |
| 2020 | Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. Abhinav Agrawal, Daniel Sheldon, Justin Domke |
| 2020 | Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin |
| 2020 | Adversarial Attacks on Deep Graph Matching. Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou |
| 2020 | Adversarial Attacks on Linear Contextual Bandits. Evrard Garcelon, Baptiste Rozière, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta |
| 2020 | Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm. Lin Yang, Mohammad Hassan Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong |
| 2020 | Adversarial Blocking Bandits. Nick Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh |
| 2020 | Adversarial Counterfactual Learning and Evaluation for Recommender System. Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan |
| 2020 | Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion. Qianqian Ma, Alex Olshevsky |
| 2020 | Adversarial Distributional Training for Robust Deep Learning. Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su |
| 2020 | Adversarial Example Games. Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton |
| 2020 | Adversarial Learning for Robust Deep Clustering. Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu |
| 2020 | Adversarial Robustness of Supervised Sparse Coding. Jeremias Sulam, Ramchandran Muthukumar, Raman Arora |
| 2020 | Adversarial Self-Supervised Contrastive Learning. Minseon Kim, Jihoon Tack, Sung Ju Hwang |
| 2020 | Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization. Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai |
| 2020 | Adversarial Sparse Transformer for Time Series Forecasting. Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang |
| 2020 | Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation. Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang |
| 2020 | Adversarial Training is a Form of Data-dependent Operator Norm Regularization. Kevin Roth, Yannic Kilcher, Thomas Hofmann |
| 2020 | Adversarial Weight Perturbation Helps Robust Generalization. Dongxian Wu, Shu-Tao Xia, Yisen Wang |
| 2020 | Adversarial robustness via robust low rank representations. Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan |
| 2020 | Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. Micah Goldblum, Liam Fowl, Tom Goldstein |
| 2020 | Adversarially Robust Streaming Algorithms via Differential Privacy. Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer |
| 2020 | Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models. Adarsh K. Jeewajee, Leslie Pack Kaelbling |
| 2020 | Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang |
| 2020 | Agnostic Learning of a Single Neuron with Gradient Descent. Spencer Frei, Yuan Cao, Quanquan Gu |
| 2020 | Agnostic Learning with Multiple Objectives. Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus |
| 2020 | Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space. Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang |
| 2020 | Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera |
| 2020 | All Word Embeddings from One Embedding. Sho Takase, Sosuke Kobayashi |
| 2020 | All your loss are belong to Bayes. Christian J. Walder, Richard Nock |
| 2020 | All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation. Jean Barbier, Nicolas Macris, Cynthia Rush |
| 2020 | Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition. Zihan Zhang, Yuan Zhou, Xiangyang Ji |
| 2020 | Almost Surely Stable Deep Dynamics. Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni |
| 2020 | An Analysis of SVD for Deep Rotation Estimation. Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia |
| 2020 | An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits. Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric |
| 2020 | An Efficient Adversarial Attack for Tree Ensembles. Chong Zhang, Huan Zhang, Cho-Jui Hsieh |
| 2020 | An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search. Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon |
| 2020 | An Efficient Framework for Clustered Federated Learning. Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran |
| 2020 | An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. Julian Katz-Samuels, Lalit Jain, Zohar S. Karnin, Kevin Jamieson |
| 2020 | An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay. Scott Fujimoto, David Meger, Doina Precup |
| 2020 | An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch. Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone |
| 2020 | An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods. Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin |
| 2020 | An Improved Analysis of Stochastic Gradient Descent with Momentum. Yanli Liu, Yuan Gao, Wotao Yin |
| 2020 | An Optimal Elimination Algorithm for Learning a Best Arm. Avinatan Hassidim, Ron Kupfer, Yaron Singer |
| 2020 | An Unbiased Risk Estimator for Learning with Augmented Classes. Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, Zhi-Hua Zhou |
| 2020 | An Unsupervised Information-Theoretic Perceptual Quality Metric. Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy T. Chinen |
| 2020 | An analytic theory of shallow networks dynamics for hinge loss classification. Franco Pellegrini, Giulio Biroli |
| 2020 | An efficient nonconvex reformulation of stagewise convex optimization problems. Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham |
| 2020 | An implicit function learning approach for parametric modal regression. Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White |
| 2020 | An operator view of policy gradient methods. Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux |
| 2020 | Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. Taira Tsuchiya, Junya Honda, Masashi Sugiyama |
| 2020 | Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry. Yossi Arjevani, Michael Field |
| 2020 | Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. Randall Balestriero, Sébastien Paris, Richard G. Baraniuk |
| 2020 | Applications of Common Entropy for Causal Inference. Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath |
| 2020 | Approximate Cross-Validation for Structured Models. Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick |
| 2020 | Approximate Cross-Validation with Low-Rank Data in High Dimensions. William T. Stephenson, Madeleine Udell, Tamara Broderick |
| 2020 | Approximate Heavily-Constrained Learning with Lagrange Multiplier Models. Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Lutong Wang, Wenshuo Guo |
| 2020 | Approximation Based Variance Reduction for Reparameterization Gradients. Tomas Geffner, Justin Domke |
| 2020 | Assessing SATNet's Ability to Solve the Symbol Grounding Problem. Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger |
| 2020 | Assisted Learning: A Framework for Multi-Organization Learning. Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan |
| 2020 | Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. Christopher Frye, Colin Rowat, Ilya Feige |
| 2020 | Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance. Ziv Goldfeld, Kristjan H. Greenewald, Kengo Kato |
| 2020 | Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model. Yiwei Shen, Pierre C. Bellec |
| 2020 | Asymptotically Optimal Exact Minibatch Metropolis-Hastings. Ruqi Zhang, A. Feder Cooper, Christopher De Sa |
| 2020 | Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos |
| 2020 | AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva |
| 2020 | Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. Isabella Pozzi, Sander M. Bohté, Pieter R. Roelfsema |
| 2020 | Attribute Prototype Network for Zero-Shot Learning. Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata |
| 2020 | Attribution Preservation in Network Compression for Reliable Network Interpretation. Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang |
| 2020 | Audeo: Audio Generation for a Silent Performance Video. Kun Su, Xiulong Liu, Eli Shlizerman |
| 2020 | Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski, Jonathan R. Ullman, Alina Oprea |
| 2020 | Auto Learning Attention. Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao |
| 2020 | Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation. Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin |
| 2020 | AutoBSS: An Efficient Algorithm for Block Stacking Style Search. Yikang Zhang, Jian Zhang, Zhao Zhong |
| 2020 | AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference. Qian Lou, Song Bian, Lei Jiang |
| 2020 | AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning. Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing |
| 2020 | Autoencoders that don't overfit towards the Identity. Harald Steck |
| 2020 | Autofocused oracles for model-based design. Clara Fannjiang, Jennifer Listgarten |
| 2020 | Automatic Curriculum Learning through Value Disagreement. Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto |
| 2020 | Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond. Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh |
| 2020 | Automatically Learning Compact Quality-aware Surrogates for Optimization Problems. Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe |
| 2020 | Autoregressive Score Matching. Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon |
| 2020 | Auxiliary Task Reweighting for Minimum-data Learning. Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu |
| 2020 | AvE: Assistance via Empowerment. Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca D. Dragan |
| 2020 | Avoiding Side Effects By Considering Future Tasks. Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg |
| 2020 | Avoiding Side Effects in Complex Environments. Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli |
| 2020 | Axioms for Learning from Pairwise Comparisons. Ritesh Noothigattu, Dominik Peters, Ariel D. Procaccia |
| 2020 | BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning. Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith W. Ross |
| 2020 | BERT Loses Patience: Fast and Robust Inference with Early Exit. Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian J. McAuley, Ke Xu, Furu Wei |
| 2020 | BOSS: Bayesian Optimization over String Spaces. Henry B. Moss, David S. Leslie, Daniel Beck, Javier González, Paul Rayson |
| 2020 | BRP-NAS: Prediction-based NAS using GCNs. Lukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, Nicholas D. Lane |
| 2020 | Backpropagating Linearly Improves Transferability of Adversarial Examples. Yiwen Guo, Qizhang Li, Hao Chen |
| 2020 | Bad Global Minima Exist and SGD Can Reach Them. Shengchao Liu, Dimitris S. Papailiopoulos, Dimitris Achlioptas |
| 2020 | Balanced Meta-Softmax for Long-Tailed Visual Recognition. Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li |
| 2020 | Bandit Linear Control. Asaf B. Cassel, Tomer Koren |
| 2020 | Bandit Samplers for Training Graph Neural Networks. Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi |
| 2020 | BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits. Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony |
| 2020 | Barking up the right tree: an approach to search over molecule synthesis DAGs. John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato |
| 2020 | Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks. Soham De, Samuel L. Smith |
| 2020 | Batch normalization provably avoids ranks collapse for randomly initialised deep networks. Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi |
| 2020 | Batched Coarse Ranking in Multi-Armed Bandits. Nikolai Karpov, Qin Zhang |
| 2020 | Baxter Permutation Process. Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda |
| 2020 | BayReL: Bayesian Relational Learning for Multi-omics Data Integration. Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Xiaoning Qian |
| 2020 | Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class. Mingyuan Zhang, Shivani Agarwal |
| 2020 | Bayesian Attention Modules. Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou |
| 2020 | Bayesian Bits: Unifying Quantization and Pruning. Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling |
| 2020 | Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks. Junsouk Choi, Robert S. Chapkin, Yang Ni |
| 2020 | Bayesian Deep Ensembles via the Neural Tangent Kernel. Bobby He, Balaji Lakshminarayanan, Yee Whye Teh |
| 2020 | Bayesian Deep Learning and a Probabilistic Perspective of Generalization. Andrew Gordon Wilson, Pavel Izmailov |
| 2020 | Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels. Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey |
| 2020 | Bayesian Multi-type Mean Field Multi-agent Imitation Learning. Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong |
| 2020 | Bayesian Optimization for Iterative Learning. Vu Nguyen, Sebastian Schulze, Michael A. Osborne |
| 2020 | Bayesian Optimization of Risk Measures. Sait Cakmak, Raul Astudillo, Peter I. Frazier, Enlu Zhou |
| 2020 | Bayesian Probabilistic Numerical Integration with Tree-Based Models. Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol |
| 2020 | Bayesian Pseudocoresets. Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell |
| 2020 | Bayesian Robust Optimization for Imitation Learning. Daniel S. Brown, Scott Niekum, Marek Petrik |
| 2020 | Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods. Laurence Aitchison |
| 2020 | Belief Propagation Neural Networks. Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon |
| 2020 | Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information. Genevieve Flaspohler, Nicholas Roy, John W. Fisher III |
| 2020 | Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method. Simiao Ren, Willie Padilla, Jordan M. Malof |
| 2020 | Benchmarking Deep Learning Interpretability in Time Series Predictions. Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi |
| 2020 | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. Hongyu Ren, Jure Leskovec |
| 2020 | Beta R-CNN: Looking into Pedestrian Detection from Another Perspective. Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang |
| 2020 | Better Full-Matrix Regret via Parameter-Free Online Learning. Ashok Cutkosky |
| 2020 | Better Set Representations For Relational Reasoning. Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser-Nam Lim, Austin R. Benson |
| 2020 | Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra |
| 2020 | Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. Kaivalya Rawal, Himabindu Lakkaraju |
| 2020 | Beyond Lazy Training for Over-parameterized Tensor Decomposition. Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge |
| 2020 | Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser |
| 2020 | Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency. Robert Geirhos, Kristof Meding, Felix A. Wichmann |
| 2020 | Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch |
| 2020 | Bi-level Score Matching for Learning Energy-based Latent Variable Models. Fan Bao, Chongxuan Li, Taufik Xu, Hang Su, Jun Zhu, Bo Zhang |
| 2020 | Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs. Chung-wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang |
| 2020 | Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. Yifan Hu, Siqi Zhang, Xin Chen, Niao He |
| 2020 | Bidirectional Convolutional Poisson Gamma Dynamical Systems. Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou |
| 2020 | Big Bird: Transformers for Longer Sequences. Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed |
| 2020 | Big Self-Supervised Models are Strong Semi-Supervised Learners. Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton |
| 2020 | Biological credit assignment through dynamic inversion of feedforward networks. William F. Podlaski, Christian K. Machens |
| 2020 | Biologically Inspired Mechanisms for Adversarial Robustness. Manish V. Reddy, Andrzej Banburski, Nishka Pant, Tomaso A. Poggio |
| 2020 | Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu |
| 2020 | Black-Box Optimization with Local Generative Surrogates. Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atilim Gunes Baydin |
| 2020 | Black-Box Ripper: Copying black-box models using generative evolutionary algorithms. Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu |
| 2020 | Blind Video Temporal Consistency via Deep Video Prior. Chenyang Lei, Yazhou Xing, Qifeng Chen |
| 2020 | BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images. Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy J. Mitra |
| 2020 | BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy |
| 2020 | Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar |
| 2020 | Boosting Adversarial Training with Hypersphere Embedding. Tianyu Pang, Xiao Yang, Yinpeng Dong, Taufik Xu, Jun Zhu, Hang Su |
| 2020 | Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates. Kaiwen Zhou, Anthony Man-Cho So, James Cheng |
| 2020 | Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko |
| 2020 | Bootstrapping neural processes. Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh |
| 2020 | Boundary thickness and robustness in learning models. Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney |
| 2020 | BoxE: A Box Embedding Model for Knowledge Base Completion. Ralph Abboud, Ismail Ilkan Ceylan, Thomas Lukasiewicz, Tommaso Salvatori |
| 2020 | Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization. Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu |
| 2020 | Breaking the Communication-Privacy-Accuracy Trilemma. Wei-Ning Chen, Peter Kairouz, Ayfer Özgür |
| 2020 | Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model. Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen |
| 2020 | Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning. Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang |
| 2020 | Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang |
| 2020 | Building powerful and equivariant graph neural networks with structural message-passing. Clément Vignac, Andreas Loukas, Pascal Frossard |
| 2020 | Byzantine Resilient Distributed Multi-Task Learning. Jiani Li, Waseem Abbas, Xenofon D. Koutsoukos |
| 2020 | CASTLE: Regularization via Auxiliary Causal Graph Discovery. Trent Kyono, Yao Zhang, Mihaela van der Schaar |
| 2020 | CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation. Makan Arastuie, Subhadeep Paul, Kevin S. Xu |
| 2020 | CLEARER: Multi-Scale Neural Architecture Search for Image Restoration. Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng |
| 2020 | CO-Optimal Transport. Titouan Vayer, Ievgen Redko, Rémi Flamary, Nicolas Courty |
| 2020 | COBE: Contextualized Object Embeddings from Narrated Instructional Video. Gedas Bertasius, Lorenzo Torresani |
| 2020 | COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning. Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, Thomas Brox |
| 2020 | COPT: Coordinated Optimal Transport on Graphs. Yihe Dong, Will Sawin |
| 2020 | COT-GAN: Generating Sequential Data via Causal Optimal Transport. Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio |
| 2020 | CSER: Communication-efficient SGD with Error Reset. Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin |
| 2020 | CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances. Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin |
| 2020 | CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations. Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas |
| 2020 | Calibrated Reliable Regression using Maximum Mean Discrepancy. Peng Cui, Wenbo Hu, Jun Zhu |
| 2020 | Calibrating CNNs for Lifelong Learning. Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai |
| 2020 | Calibrating Deep Neural Networks using Focal Loss. Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania |
| 2020 | Calibration of Shared Equilibria in General Sum Partially Observable Markov Games. Nelson Vadori, Sumitra Ganesh, Prashant P. Reddy, Manuela Veloso |
| 2020 | Can Graph Neural Networks Count Substructures? Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna |
| 2020 | Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. Disi Ji, Padhraic Smyth, Mark Steyvers |
| 2020 | Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni |
| 2020 | Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro |
| 2020 | Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory. Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang |
| 2020 | Can the Brain Do Backpropagation? - Exact Implementation of Backpropagation in Predictive Coding Networks. Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz |
| 2020 | Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction. David Novotný, Roman Shapovalov, Andrea Vedaldi |
| 2020 | Cascaded Text Generation with Markov Transformers. Yuntian Deng, Alexander M. Rush |
| 2020 | Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim |
| 2020 | Causal Discovery in Physical Systems from Videos. Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg |
| 2020 | Causal Estimation with Functional Confounders. Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath |
| 2020 | Causal Imitation Learning With Unobserved Confounders. Junzhe Zhang, Daniel Kumor, Elias Bareinboim |
| 2020 | Causal Intervention for Weakly-Supervised Semantic Segmentation. Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun |
| 2020 | Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen |
| 2020 | Causal analysis of Covid-19 Spread in Germany. Atalanti-Anastasia Mastakouri, Bernhard Schölkopf |
| 2020 | Certifiably Adversarially Robust Detection of Out-of-Distribution Data. Julian Bitterwolf, Alexander Meinke, Matthias Hein |
| 2020 | Certified Defense to Image Transformations via Randomized Smoothing. Marc Fischer, Maximilian Baader, Martin T. Vechev |
| 2020 | Certified Monotonic Neural Networks. Xingchao Liu, Xing Han, Na Zhang, Qiang Liu |
| 2020 | Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks. Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang |
| 2020 | Certifying Confidence via Randomized Smoothing. Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein |
| 2020 | Certifying Strategyproof Auction Networks. Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson |
| 2020 | Chaos, Extremism and Optimism: Volume Analysis of Learning in Games. Yun Kuen Cheung, Georgios Piliouras |
| 2020 | Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. Sanghack Lee, Elias Bareinboim |
| 2020 | Characterizing emergent representations in a space of candidate learning rules for deep networks. Yinan Cao, Christopher Summerfield, Andrew M. Saxe |
| 2020 | Choice Bandits. Arpit Agarwal, Nicholas Johnson, Shivani Agarwal |
| 2020 | CircleGAN: Generative Adversarial Learning across Spherical Circles. Woohyeon Shim, Minsu Cho |
| 2020 | Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2020 | Classification with Valid and Adaptive Coverage. Yaniv Romano, Matteo Sesia, Emmanuel J. Candès |
| 2020 | Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. Didrik Nielsen, Ole Winther |
| 2020 | Co-Tuning for Transfer Learning. Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang |
| 2020 | Co-exposure Maximization in Online Social Networks. Sijing Tu, Çigdem Aslay, Aristides Gionis |
| 2020 | CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection. Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao |
| 2020 | CoMIR: Contrastive Multimodal Image Representation for Registration. Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje |
| 2020 | CoSE: Compositional Stroke Embeddings. Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges |
| 2020 | CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching. Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu |
| 2020 | Coded Sequential Matrix Multiplication For Straggler Mitigation. M. Nikhil Krishnan, Seyederfan Hosseini, Ashish Khisti |
| 2020 | CogLTX: Applying BERT to Long Texts. Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang |
| 2020 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic |
| 2020 | Coherent Hierarchical Multi-Label Classification Networks. Eleonora Giunchiglia, Thomas Lukasiewicz |
| 2020 | CoinDICE: Off-Policy Confidence Interval Estimation. Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans |
| 2020 | CoinPress: Practical Private Mean and Covariance Estimation. Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman |
| 2020 | ColdGANs: Taming Language GANs with Cautious Sampling Strategies. Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano |
| 2020 | Collapsing Bandits and Their Application to Public Health Intervention. Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe |
| 2020 | Collegial Ensembles. Etai Littwin, Ben Myara, Sima Sabah, Joshua M. Susskind, Shuangfei Zhai, Oren Golan |
| 2020 | Color Visual Illusions: A Statistics-based Computational Model. Elad Hirsch, Ayellet Tal |
| 2020 | Combining Deep Reinforcement Learning and Search for Imperfect-Information Games. Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong |
| 2020 | Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian. Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay |
| 2020 | Community detection using fast low-cardinality semidefinite programming
. Po-Wei Wang, J. Zico Kolter |
| 2020 | CompRess: Self-Supervised Learning by Compressing Representations. Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash |
| 2020 | Compact task representations as a normative model for higher-order brain activity. Severin Berger, Christian K. Machens |
| 2020 | Comparator-Adaptive Convex Bandits. Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo |
| 2020 | Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová |
| 2020 | Compositional Explanations of Neurons. Jesse Mu, Jacob Andreas |
| 2020 | Compositional Generalization by Learning Analytical Expressions. Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang |
| 2020 | Compositional Generalization via Neural-Symbolic Stack Machines. Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou |
| 2020 | Compositional Visual Generation with Energy Based Models. Yilun Du, Shuang Li, Igor Mordatch |
| 2020 | Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition. Dat Huynh, Ehsan Elhamifar |
| 2020 | Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection. Zeyi Huang, Yang Zou, B. V. K. Vijaya Kumar, Dong Huang |
| 2020 | Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato |
| 2020 | Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming. Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi |
| 2020 | Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds. Hassan Hafez-Kolahi, Zeinab Golgooni, Shohreh Kasaei, Mahdieh Soleymani |
| 2020 | Confidence sequences for sampling without replacement. Ian Waudby-Smith, Aaditya Ramdas |
| 2020 | Conformal Symplectic and Relativistic Optimization. Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal |
| 2020 | Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. Nathan Kallus, Angela Zhou |
| 2020 | Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices. John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye |
| 2020 | Consequences of Misaligned AI. Simon Zhuang, Dylan Hadfield-Menell |
| 2020 | Conservative Q-Learning for Offline Reinforcement Learning. Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine |
| 2020 | Consistency Regularization for Certified Robustness of Smoothed Classifiers. Jongheon Jeong, Jinwoo Shin |
| 2020 | Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. Alexander Ritchie, Robert A. Vandermeulen, Clayton D. Scott |
| 2020 | Consistent Plug-in Classifiers for Complex Objectives and Constraints. Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan |
| 2020 | Consistent Structural Relation Learning for Zero-Shot Segmentation. Peike Li, Yunchao Wei, Yi Yang |
| 2020 | Consistent feature selection for analytic deep neural networks. Vu C. Dinh, Lam Si Tung Ho |
| 2020 | Constant-Expansion Suffices for Compressed Sensing with Generative Priors. Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis |
| 2020 | Constrained episodic reinforcement learning in concave-convex and knapsack settings. Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun |
| 2020 | Constraining Variational Inference with Geometric Jensen-Shannon Divergence. Jacob Deasy, Nikola Simidjievski, Pietro Lió |
| 2020 | Content Provider Dynamics and Coordination in Recommendation Ecosystems. Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz |
| 2020 | Contextual Games: Multi-Agent Learning with Side Information. Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour |
| 2020 | Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming. Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab S. Mirrokni |
| 2020 | Continual Deep Learning by Functional Regularisation of Memorable Past. Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan |
| 2020 | Continual Learning in Low-rank Orthogonal Subspaces. Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr |
| 2020 | Continual Learning of Control Primitives : Skill Discovery via Reset-Games. Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine |
| 2020 | Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks. Zixuan Ke, Bing Liu, Xingchang Huang |
| 2020 | Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon |
| 2020 | Continuous Meta-Learning without Tasks. James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone |
| 2020 | Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision. Nicolai Häni, Selim Engin, Jun-Jee Chao, Volkan Isler |
| 2020 | Continuous Regularized Wasserstein Barycenters. Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon |
| 2020 | Continuous Submodular Maximization: Beyond DR-Submodularity. Moran Feldman, Amin Karbasi |
| 2020 | Continuous Surface Embeddings. Natalia Neverova, David Novotný, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi |
| 2020 | ContraGAN: Contrastive Learning for Conditional Image Generation. Minguk Kang, Jaesik Park |
| 2020 | Contrastive Learning with Adversarial Examples. Chih-Hui Ho, Nuno Vasconcelos |
| 2020 | Contrastive learning of global and local features for medical image segmentation with limited annotations. Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu |
| 2020 | ConvBERT: Improving BERT with Span-based Dynamic Convolution. Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan |
| 2020 | Convergence and Stability of Graph Convolutional Networks on Large Random Graphs. Nicolas Keriven, Alberto Bietti, Samuel Vaiter |
| 2020 | Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters. Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor |
| 2020 | Convex optimization based on global lower second-order models. Nikita Doikov, Yurii E. Nesterov |
| 2020 | Convolutional Generation of Textured 3D Meshes. Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi |
| 2020 | Convolutional Tensor-Train LSTM for Spatio-Temporal Learning. Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar |
| 2020 | Cooperative Heterogeneous Deep Reinforcement Learning. Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang |
| 2020 | Cooperative Multi-player Bandit Optimization. Ilai Bistritz, Nicholas Bambos |
| 2020 | Coresets for Near-Convex Functions. Murad Tukan, Alaa Maalouf, Dan Feldman |
| 2020 | Coresets for Regressions with Panel Data. Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi |
| 2020 | Coresets for Robust Training of Deep Neural Networks against Noisy Labels. Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec |
| 2020 | Coresets via Bilevel Optimization for Continual Learning and Streaming. Zalán Borsos, Mojmir Mutny, Andreas Krause |
| 2020 | Correlation Robust Influence Maximization. Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan |
| 2020 | Correspondence learning via linearly-invariant embedding. Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov |
| 2020 | Counterexample-Guided Learning of Monotonic Neural Networks. Aishwarya Sivaraman, Golnoosh Farnadi, Todd D. Millstein, Guy Van den Broeck |
| 2020 | Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding. Zhu Zhang, Zhou Zhao, Zhijie Lin, Jieming Zhu, Xiuqiang He |
| 2020 | Counterfactual Data Augmentation using Locally Factored Dynamics. Silviu Pitis, Elliot Creager, Animesh Garg |
| 2020 | Counterfactual Prediction for Bundle Treatment. Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He |
| 2020 | Counterfactual Predictions under Runtime Confounding. Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova |
| 2020 | Counterfactual Vision-and-Language Navigation: Unravelling the Unseen. Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton van den Hengel |
| 2020 | Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama |
| 2020 | Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search. Houwen Peng, Hao Du, Hongyuan Yu, Qi Li, Jing Liao, Jianlong Fu |
| 2020 | Critic Regularized Regression. Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas |
| 2020 | Cross-Scale Internal Graph Neural Network for Image Super-Resolution. Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy |
| 2020 | Cross-lingual Retrieval for Iterative Self-Supervised Training. Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu |
| 2020 | Cross-validation Confidence Intervals for Test Error. Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey |
| 2020 | CrossTransformers: spatially-aware few-shot transfer. Carl Doersch, Ankush Gupta, Andrew Zisserman |
| 2020 | Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. Kwang-Sung Jun, Chicheng Zhang |
| 2020 | CryptoNAS: Private Inference on a ReLU Budget. Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg |
| 2020 | Curriculum By Smoothing. Samarth Sinha, Animesh Garg, Hugo Larochelle |
| 2020 | Curriculum Learning by Dynamic Instance Hardness. Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes |
| 2020 | Curriculum learning for multilevel budgeted combinatorial problems. Adel Nabli, Margarida Carvalho |
| 2020 | Curvature Regularization to Prevent Distortion in Graph Embedding. Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang |
| 2020 | Cycle-Contrast for Self-Supervised Video Representation Learning. Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami |
| 2020 | DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. Dennis Wei, Tian Gao, Yue Yu |
| 2020 | DISK: Learning local features with policy gradient. Michal J. Tyszkiewicz, Pascal Fua, Eduard Trulls |
| 2020 | DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles. Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Hai Li |
| 2020 | Dark Experience for General Continual Learning: a Strong, Simple Baseline. Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara |
| 2020 | Data Diversification: A Simple Strategy For Neural Machine Translation. Xuan-Phi Nguyen, Shafiq R. Joty, Kui Wu, Ai Ti Aw |
| 2020 | De-Anonymizing Text by Fingerprinting Language Generation. Zhen Sun, Roei Schuster, Vitaly Shmatikov |
| 2020 | Debiased Contrastive Learning. Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka |
| 2020 | Debiasing Averaged Stochastic Gradient Descent to handle missing values. Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse |
| 2020 | Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney |
| 2020 | Debugging Tests for Model Explanations. Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim |
| 2020 | Decentralized Accelerated Proximal Gradient Descent. Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang |
| 2020 | Decentralized Langevin Dynamics for Bayesian Learning. Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram |
| 2020 | Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. Gang Wang, Songtao Lu, Georgios B. Giannakis, Gerald Tesauro, Jian Sun |
| 2020 | Decision trees as partitioning machines to characterize their generalization properties. Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand |
| 2020 | Decision-Making with Auto-Encoding Variational Bayes. Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier |
| 2020 | Decisions, Counterfactual Explanations and Strategic Behavior. Stratis Tsirtsis, Manuel Gomez Rodriguez |
| 2020 | Deep Archimedean Copulas. Chun Kai Ling, Fei Fang, J. Zico Kolter |
| 2020 | Deep Automodulators. Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin |
| 2020 | Deep Diffusion-Invariant Wasserstein Distributional Classification. Sung Woo Park, Dong Wook Shu, Junseok Kwon |
| 2020 | Deep Direct Likelihood Knockoffs. Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath |
| 2020 | Deep Energy-based Modeling of Discrete-Time Physics. Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi |
| 2020 | Deep Evidential Regression. Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus |
| 2020 | Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. Anqi Wu, Estefany Kelly Buchanan, Matthew R. Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora E. Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski |
| 2020 | Deep Imitation Learning for Bimanual Robotic Manipulation. Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson L. S. Wong, Rose Yu |
| 2020 | Deep Inverse Q-learning with Constraints. Gabriel Kalweit, Maria Hügle, Moritz Werling, Joschka Boedecker |
| 2020 | Deep Metric Learning with Spherical Embedding. Dingyi Zhang, Yingming Li, Zhongfei Zhang |
| 2020 | Deep Multimodal Fusion by Channel Exchanging. Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang |
| 2020 | Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus |
| 2020 | Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games. Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang |
| 2020 | Deep Reinforcement and InfoMax Learning. Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm |
| 2020 | Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network. Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou |
| 2020 | Deep Shells: Unsupervised Shape Correspondence with Optimal Transport. Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers |
| 2020 | Deep Smoothing of the Implied Volatility Surface. Damien Ackerer, Natasa Tagasovska, Thibault Vatter |
| 2020 | Deep Statistical Solvers. Balthazar Donon, Zhengying Liu, Wenzhuo Liu, Isabelle Guyon, Antoine Marot, Marc Schoenauer |
| 2020 | Deep Structural Causal Models for Tractable Counterfactual Inference. Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker |
| 2020 | Deep Subspace Clustering with Data Augmentation. Mahdi Abavisani, Alireza Naghizadeh, Dimitris N. Metaxas, Vishal M. Patel |
| 2020 | Deep Transformation-Invariant Clustering. Tom Monnier, Thibault Groueix, Mathieu Aubry |
| 2020 | Deep Transformers with Latent Depth. Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong |
| 2020 | Deep Variational Instance Segmentation. Jialin Yuan, Chao Chen, Fuxin Li |
| 2020 | Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring. Jiangxin Dong, Stefan Roth, Bernt Schiele |
| 2020 | Deep active inference agents using Monte-Carlo methods. Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl J. Friston |
| 2020 | Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli |
| 2020 | Deep reconstruction of strange attractors from time series. William Gilpin |
| 2020 | DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs. Yaxing Wang, Lu Yu, Joost van de Weijer |
| 2020 | DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation. Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte |
| 2020 | Deeply Learned Spectral Total Variation Decomposition. Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola B. Schönlieb |
| 2020 | Delay and Cooperation in Nonstochastic Linear Bandits. Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2020 | Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. Juhan Bae, Roger B. Grosse |
| 2020 | Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation. Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin |
| 2020 | Demixed shared component analysis of neural population data from multiple brain areas. Yu Takagi, Steven W. Kennerley, Jun-ichiro Hirayama, Laurence T. Hunt |
| 2020 | Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases. Senthil Purushwalkam, Abhinav Gupta |
| 2020 | Demystifying Orthogonal Monte Carlo and Beyond. Han Lin, Haoxian Chen, Krzysztof Marcin Choromanski, Tianyi Zhang, Clement Laroche |
| 2020 | Denoised Smoothing: A Provable Defense for Pretrained Classifiers. Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter |
| 2020 | Denoising Diffusion Probabilistic Models. Jonathan Ho, Ajay Jain, Pieter Abbeel |
| 2020 | Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs. Xiangru Huang, Haitao Yang, Etienne Vouga, Qixing Huang |
| 2020 | Depth Uncertainty in Neural Networks. Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato |
| 2020 | Design Space for Graph Neural Networks. Jiaxuan You, Zhitao Ying, Jure Leskovec |
| 2020 | Detecting Hands and Recognizing Physical Contact in the Wild. Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai Nguyen |
| 2020 | Detecting Interactions from Neural Networks via Topological Analysis. Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu |
| 2020 | Detection as Regression: Certified Object Detection with Median Smoothing. Ping-Yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein |
| 2020 | Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time. Kai Han, Zongmai Cao, Shuang Cui, Benwei Wu |
| 2020 | Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data. Michael Cogswell, Jiasen Lu, Rishabh Jain, Stefan Lee, Devi Parikh, Dhruv Batra |
| 2020 | DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling. Moshe Eliasof, Eran Treister |
| 2020 | Differentiable Augmentation for Data-Efficient GAN Training. Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han |
| 2020 | Differentiable Causal Discovery from Interventional Data. Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin |
| 2020 | Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Samuel Daulton, Maximilian Balandat, Eytan Bakshy |
| 2020 | Differentiable Meta-Learning of Bandit Policies. Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer |
| 2020 | Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement. Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, Steven W. Su |
| 2020 | Differentiable Top-k with Optimal Transport. Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister |
| 2020 | Differentially Private Clustering: Tight Approximation Ratios. Badih Ghazi, Ravi Kumar, Pasin Manurangsi |
| 2020 | Differentially-Private Federated Linear Bandits. Abhimanyu Dubey, Alex 'Sandy' Pentland |
| 2020 | Digraph Inception Convolutional Networks. Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, Andrew Lim |
| 2020 | Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala |
| 2020 | Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow |
| 2020 | Directional Pruning of Deep Neural Networks. Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng |
| 2020 | Directional convergence and alignment in deep learning. Ziwei Ji, Matus Telgarsky |
| 2020 | Dirichlet Graph Variational Autoencoder. Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang |
| 2020 | DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. Zhe Dong, Andriy Mnih, George Tucker |
| 2020 | DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. Aviral Kumar, Abhishek Gupta, Sergey Levine |
| 2020 | Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation. Kwanyong Park, Sanghyun Woo, Inkyu Shin, In So Kweon |
| 2020 | Discovering Reinforcement Learning Algorithms. Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver |
| 2020 | Discovering Symbolic Models from Deep Learning with Inductive Biases. Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho |
| 2020 | Discovering conflicting groups in signed networks. Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis |
| 2020 | Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching. Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou |
| 2020 | Disentangling Human Error from Ground Truth in Segmentation of Medical Images. Le Zhang, Ryutaro Tanno, Moucheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel C. Alexander |
| 2020 | Disentangling by Subspace Diffusion. David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière |
| 2020 | Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation. Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong Li |
| 2020 | Dissecting Neural ODEs. Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama |
| 2020 | Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec |
| 2020 | Distributed Distillation for On-Device Learning. Ilai Bistritz, Ariana J. Mann, Nicholas Bambos |
| 2020 | Distributed Newton Can Communicate Less and Resist Byzantine Workers. Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar |
| 2020 | Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong |
| 2020 | Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning. Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin |
| 2020 | Distribution Matching for Crowd Counting. Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen |
| 2020 | Distribution-free binary classification: prediction sets, confidence intervals and calibration. Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas |
| 2020 | Distributional Robustness with IPMs and links to Regularization and GANs. Hisham Husain |
| 2020 | Distributionally Robust Federated Averaging. Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi |
| 2020 | Distributionally Robust Local Non-parametric Conditional Estimation. Viet Anh Nguyen, Fan Zhang, José H. Blanchet, Erick Delage, Yinyu Ye |
| 2020 | Distributionally Robust Parametric Maximum Likelihood Estimation. Viet Anh Nguyen, Xuhui Zhang, José H. Blanchet, Angelos Georghiou |
| 2020 | Diverse Image Captioning with Context-Object Split Latent Spaces. Shweta Mahajan, Stefan Roth |
| 2020 | Diversity can be Transferred: Output Diversification for White- and Black-box Attacks. Yusuke Tashiro, Yang Song, Stefano Ermon |
| 2020 | Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations. Mina Konakovic-Lukovic, Yunsheng Tian, Wojciech Matusik |
| 2020 | Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry |
| 2020 | Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang |
| 2020 | Domain Adaptation as a Problem of Inference on Graphical Models. Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour |
| 2020 | Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon |
| 2020 | Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization. Haoliang Li, Yufei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, Alex C. Kot |
| 2020 | Domain Generalization via Entropy Regularization. Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao |
| 2020 | Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. Nathan Kallus, Masatoshi Uehara |
| 2020 | Dual Instrumental Variable Regression. Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj |
| 2020 | Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi |
| 2020 | Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama |
| 2020 | Dual-Free Stochastic Decentralized Optimization with Variance Reduction. Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié |
| 2020 | Dual-Resolution Correspondence Networks. Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu |
| 2020 | Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. Zhanqiu Zhang, Jianyu Cai, Jie Wang |
| 2020 | DynaBERT: Dynamic BERT with Adaptive Width and Depth. Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu |
| 2020 | Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake |
| 2020 | Dynamic Regret of Convex and Smooth Functions. Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou |
| 2020 | Dynamic Regret of Policy Optimization in Non-Stationary Environments. Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie |
| 2020 | Dynamic Submodular Maximization. Morteza Monemizadeh |
| 2020 | Dynamic allocation of limited memory resources in reinforcement learning. Nisheet Patel, Luigi Acerbi, Alexandre Pouget |
| 2020 | Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová |
| 2020 | Early-Learning Regularization Prevents Memorization of Noisy Labels. Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda |
| 2020 | EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints. Sachin Chauhan, Kashish Bansal, Rijurekha Sen |
| 2020 | Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization. Jonathan Lacotte, Mert Pilanci |
| 2020 | Effective Diversity in Population Based Reinforcement Learning. Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts |
| 2020 | Efficient Algorithms for Device Placement of DNN Graph Operators. Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino |
| 2020 | Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut. Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li |
| 2020 | Efficient Clustering for Stretched Mixtures: Landscape and Optimality. Kaizheng Wang, Yuling Yan, Mateo Díaz |
| 2020 | Efficient Contextual Bandits with Continuous Actions. Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins |
| 2020 | Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning. Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, N. V. Vinodchandran |
| 2020 | Efficient Exact Verification of Binarized Neural Networks. Kai Jia, Martin C. Rinard |
| 2020 | Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization. Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh |
| 2020 | Efficient Generation of Structured Objects with Constrained Adversarial Networks. Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini |
| 2020 | Efficient Learning of Discrete Graphical Models. Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov |
| 2020 | Efficient Learning of Generative Models via Finite-Difference Score Matching. Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu |
| 2020 | Efficient Low Rank Gaussian Variational Inference for Neural Networks. Marcin Tomczak, Siddharth Swaroop, Richard E. Turner |
| 2020 | Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. Gonçalo M. Correia, Vlad Niculae, Wilker Aziz, André F. T. Martins |
| 2020 | Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. Sebastian Curi, Felix Berkenkamp, Andreas Krause |
| 2020 | Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett |
| 2020 | Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent. Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis |
| 2020 | Efficient Planning in Large MDPs with Weak Linear Function Approximation. Roshan Shariff, Csaba Szepesvári |
| 2020 | Efficient Projection-free Algorithms for Saddle Point Problems. Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu |
| 2020 | Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. Jincheng Bai, Qifan Song, Guang Cheng |
| 2020 | Efficient active learning of sparse halfspaces with arbitrary bounded noise. Chicheng Zhang, Jie Shen, Pranjal Awasthi |
| 2020 | Efficient estimation of neural tuning during naturalistic behavior. Edoardo Balzani, Kaushik J. Lakshminarasimhan, Dora E. Angelaki, Cristina Savin |
| 2020 | Efficient semidefinite-programming-based inference for binary and multi-class MRFs. Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter |
| 2020 | Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data. Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee |
| 2020 | Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks. Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon |
| 2020 | Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre M. Bayen, Stuart Russell, Andrew Critch, Sergey Levine |
| 2020 | Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences. Bowen Baker |
| 2020 | Empirical Likelihood for Contextual Bandits. Nikos Karampatziakis, John Langford, Paul Mineiro |
| 2020 | Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli |
| 2020 | End-to-End Learning and Intervention in Games. Jiayang Li, Jing Yu, Yu Marco Nie, Zhaoran Wang |
| 2020 | Energy-based Out-of-distribution Detection. Weitang Liu, Xiaoyun Wang, John D. Owens, Yixuan Li |
| 2020 | Ensemble Distillation for Robust Model Fusion in Federated Learning. Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi |
| 2020 | Ensembling geophysical models with Bayesian Neural Networks. Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young |
| 2020 | Ensuring Fairness Beyond the Training Data. Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel J. Hsu |
| 2020 | Entropic Causal Inference: Identifiability and Finite Sample Results. Spencer Compton, Murat Kocaoglu, Kristjan H. Greenewald, Dmitriy Katz |
| 2020 | Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form. Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi |
| 2020 | Entrywise convergence of iterative methods for eigenproblems. Vasileios Charisopoulos, Austin R. Benson, Anil Damle |
| 2020 | Equivariant Networks for Hierarchical Structures. Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh |
| 2020 | Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. Nikolaos Karalias, Andreas Loukas |
| 2020 | Error Bounds of Imitating Policies and Environments. Tian Xu, Ziniu Li, Yang Yu |
| 2020 | Escaping Saddle-Point Faster under Interpolation-like Conditions. Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra |
| 2020 | Escaping the Gravitational Pull of Softmax. Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans |
| 2020 | Estimating Fluctuations in Neural Representations of Uncertain Environments. Sahand Farhoodi, Mark Plitt, Lisa M. Giocomo, Uri T. Eden |
| 2020 | Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks. Jingqiu Ding, Samuel B. Hopkins, David Steurer |
| 2020 | Estimating Training Data Influence by Tracing Gradient Descent. Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan |
| 2020 | Estimating decision tree learnability with polylogarithmic sample complexity. Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan |
| 2020 | Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. Ioana Bica, James Jordon, Mihaela van der Schaar |
| 2020 | Estimating weighted areas under the ROC curve. Andreas Maurer, Massimiliano Pontil |
| 2020 | Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data. Aude Sportisse, Claire Boyer, Julie Josse |
| 2020 | Estimation of Skill Distribution from a Tournament. Ali Jadbabaie, Anuran Makur, Devavrat Shah |
| 2020 | Evaluating Attribution for Graph Neural Networks. Benjamín Sánchez-Lengeling, Jennifer N. Wei, Brian K. Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy J. Colwell, Alexander B. Wiltschko |
| 2020 | Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions. Tom Yan, Christian Kroer, Alexander Peysakhovich |
| 2020 | Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization. Qi Chen, Lin Sun, Ernest Cheung, Alan L. Yuille |
| 2020 | Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders. Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone |
| 2020 | EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning. Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi |
| 2020 | Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation. Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky |
| 2020 | Evolving Normalization-Activation Layers. Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le |
| 2020 | Exact Recovery of Mangled Clusters with Same-Cluster Queries. Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice |
| 2020 | Exact expressions for double descent and implicit regularization via surrogate random design. Michal Derezinski, Feynman T. Liang, Michael W. Mahoney |
| 2020 | Exactly Computing the Local Lipschitz Constant of ReLU Networks. Matt Jordan, Alexandros G. Dimakis |
| 2020 | Exchangeable Neural ODE for Set Modeling. Yang Li, Haidong Yi, Christopher M. Bender, Siyuan Shan, Junier B. Oliva |
| 2020 | Exemplar Guided Active Learning. Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz |
| 2020 | Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation. Sajad Norouzi, David J. Fleet, Mohammad Norouzi |
| 2020 | ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks. Shuxuan Guo, José M. Álvarez, Mathieu Salzmann |
| 2020 | Experimental design for MRI by greedy policy search. Tim Bakker, Herke van Hoof, Max Welling |
| 2020 | Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation. Aaron Sonabend W., Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits |
| 2020 | Explainable Voting. Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou |
| 2020 | Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay. João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska |
| 2020 | Explicit Regularisation in Gaussian Noise Injections. Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes |
| 2020 | Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov |
| 2020 | Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil |
| 2020 | Exploiting the Surrogate Gap in Online Multiclass Classification. Dirk van der Hoeven |
| 2020 | Exploiting weakly supervised visual patterns to learn from partial annotations. Kaustav Kundu, Joseph Tighe |
| 2020 | Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling. Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos |
| 2020 | Exponential ergodicity of mirror-Langevin diffusions. Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme |
| 2020 | Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate. Akifumi Okuno, Hidetoshi Shimodaira |
| 2020 | FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun |
| 2020 | Factor Graph Grammars. David Chiang, Darcey Riley |
| 2020 | Factor Graph Neural Networks. Zhen Zhang, Fan Wu, Wee Sun Lee |
| 2020 | Factorizable Graph Convolutional Networks. Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang |
| 2020 | Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses. R. James Cotton, Fabian H. Sinz, Andreas S. Tolias |
| 2020 | Fair Hierarchical Clustering. Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang |
| 2020 | Fair Multiple Decision Making Through Soft Interventions. Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu |
| 2020 | Fair Performance Metric Elicitation. Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo |
| 2020 | Fair regression via plug-in estimator and recalibration with statistical guarantees. Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil |
| 2020 | Fair regression with Wasserstein barycenters. Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil |
| 2020 | Fairness constraints can help exact inference in structured prediction. Kevin Bello, Jean Honorio |
| 2020 | Fairness in Streaming Submodular Maximization: Algorithms and Hardness. Marwa El Halabi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski |
| 2020 | Fairness with Overlapping Groups; a Probabilistic Perspective. Forest Yang, Mouhamadou Cisse, Oluwasanmi Koyejo |
| 2020 | Fairness without Demographics through Adversarially Reweighted Learning. Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi |
| 2020 | Faithful Embeddings for Knowledge Base Queries. Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira, William W. Cohen |
| 2020 | Falcon: Fast Spectral Inference on Encrypted Data. Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang |
| 2020 | Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint. Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser |
| 2020 | Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms. Sascha Saralajew, Lars Holdijk, Thomas Villmann |
| 2020 | Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev. Xiao Wang, Qi Lei, Ioannis Panageas |
| 2020 | Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine. Jiajin Li, Caihua Chen, Anthony Man-Cho So |
| 2020 | Fast Fourier Convolution. Lu Chi, Borui Jiang, Yadong Mu |
| 2020 | Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner |
| 2020 | Fast Transformers with Clustered Attention. Apoorv Vyas, Angelos Katharopoulos, François Fleuret |
| 2020 | Fast Unbalanced Optimal Transport on a Tree. Ryoma Sato, Makoto Yamada, Hisashi Kashima |
| 2020 | Fast and Accurate $k$-means++ via Rejection Sampling. Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson |
| 2020 | Fast and Flexible Temporal Point Processes with Triangular Maps. Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann |
| 2020 | Fast geometric learning with symbolic matrices. Jean Feydy, Joan Alexis Glaunès, Benjamin Charlier, Michael M. Bronstein |
| 2020 | Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola |
| 2020 | Faster DBSCAN via subsampled similarity queries. Heinrich Jiang, Jennifer Jang, Jakub Lacki |
| 2020 | Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. Arun Ganesh, Kunal Talwar |
| 2020 | Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas |
| 2020 | Faster Wasserstein Distance Estimation with the Sinkhorn Divergence. Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré |
| 2020 | Feature Importance Ranking for Deep Learning. Maksymilian Wojtas, Ke Chen |
| 2020 | Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests. Sean Kulinski, Saurabh Bagchi, David I. Inouye |
| 2020 | FedSplit: an algorithmic framework for fast federated optimization. Reese Pathak, Martin J. Wainwright |
| 2020 | Federated Accelerated Stochastic Gradient Descent. Honglin Yuan, Tengyu Ma |
| 2020 | Federated Bayesian Optimization via Thompson Sampling. Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2020 | Federated Principal Component Analysis. Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft, Cecilia Mascolo |
| 2020 | Few-Cost Salient Object Detection with Adversarial-Paced Learning. Dingwen Zhang, Haibin Tian, Jungong Han |
| 2020 | Few-shot Image Generation with Elastic Weight Consolidation. Yijun Li, Richard Zhang, Jingwan Lu, Eli Shechtman |
| 2020 | Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning. Youngsung Kim, Jinwoo Shin, Eunho Yang, Sung Ju Hwang |
| 2020 | Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies. Yuehua Zhu, Muli Yang, Cheng Deng, Wei Liu |
| 2020 | Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin |
| 2020 | Field-wise Learning for Multi-field Categorical Data. Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu |
| 2020 | Fighting Copycat Agents in Behavioral Cloning from Observation Histories. Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao |
| 2020 | Finding All $\epsilon$-Good Arms in Stochastic Bandits. Blake Mason, Lalit K. Jain, Ardhendu Tripathy, Robert Nowak |
| 2020 | Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong |
| 2020 | Finding the Homology of Decision Boundaries with Active Learning. Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha |
| 2020 | Fine-Grained Dynamic Head for Object Detection. Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng |
| 2020 | Finer Metagenomic Reconstruction via Biodiversity Optimization. Simon Foucart, David Koslicki |
| 2020 | Finite Continuum-Armed Bandits. Solenne Gaucher |
| 2020 | Finite Versus Infinite Neural Networks: an Empirical Study. Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein |
| 2020 | Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes. Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam |
| 2020 | Finite-Time Analysis for Double Q-learning. Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang |
| 2020 | Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards. Vrettos Moulos |
| 2020 | Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu |
| 2020 | First Order Constrained Optimization in Policy Space. Yiming Zhang, Quan Vuong, Keith W. Ross |
| 2020 | First-Order Methods for Large-Scale Market Equilibrium Computation. Yuan Gao, Christian Kroer |
| 2020 | FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li |
| 2020 | Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm. Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael I. Jordan |
| 2020 | FleXOR: Trainable Fractional Quantization. Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun |
| 2020 | Flexible mean field variational inference using mixtures of non-overlapping exponential families. Jeffrey P. Spence |
| 2020 | Flows for simultaneous manifold learning and density estimation. Johann Brehmer, Kyle Cranmer |
| 2020 | Focus of Attention Improves Information Transfer in Visual Features. Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori |
| 2020 | Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. Arun Sai Suggala, Praneeth Netrapalli |
| 2020 | Forethought and Hindsight in Credit Assignment. Veronica Chelu, Doina Precup, Hado van Hasselt |
| 2020 | Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes. Juan Luis Gonzalez Bello, Munchurl Kim |
| 2020 | Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng |
| 2020 | Fourier Sparse Leverage Scores and Approximate Kernel Learning. Tamás Erdélyi, Cameron Musco, Christopher Musco |
| 2020 | Fourier Spectrum Discrepancies in Deep Network Generated Images. Tarik Dzanic, Karan Shah, Freddie D. Witherden |
| 2020 | Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics. Alex Tseng, Avanti Shrikumar, Anshul Kundaje |
| 2020 | FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training. Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin |
| 2020 | From Boltzmann Machines to Neural Networks and Back Again. Surbhi Goel, Adam R. Klivans, Frederic Koehler |
| 2020 | From Finite to Countable-Armed Bandits. Anand Kalvit, Assaf Zeevi |
| 2020 | From Predictions to Decisions: Using Lookahead Regularization. Nir Rosenfeld, Sophie Hilgard, Sai Srivatsa Ravindranath, David C. Parkes |
| 2020 | From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré |
| 2020 | FrugalML: How to use ML Prediction APIs more accurately and cheaply. Lingjiao Chen, Matei Zaharia, James Y. Zou |
| 2020 | Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels. Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason M. Saragih, Hao Li, Yaser Sheikh |
| 2020 | Fully Dynamic Algorithm for Constrained Submodular Optimization. Silvio Lattanzi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam |
| 2020 | Functional Regularization for Representation Learning: A Unified Theoretical Perspective. Siddhant Garg, Yingyu Liang |
| 2020 | Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le |
| 2020 | Further Analysis of Outlier Detection with Deep Generative Models. Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu |
| 2020 | GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni |
| 2020 | GAN Memory with No Forgetting. Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin |
| 2020 | GANSpace: Discovering Interpretable GAN Controls. Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris |
| 2020 | GCN meets GPU: Decoupling "When to Sample" from "How to Sample". Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut T. Kandemir |
| 2020 | GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs. Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj K. Singh |
| 2020 | GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. Xiang Zhang, Marinka Zitnik |
| 2020 | GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network. Prune Truong, Martin Danelljan, Luc Van Gool, Radu Timofte |
| 2020 | GPS-Net: Graph-based Photometric Stereo Network. Zhuokun Yao, Kun Li, Ying Fu, Haofeng Hu, Boxin Shi |
| 2020 | GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification. John T. Halloran, David M. Rocke |
| 2020 | GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis. Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger |
| 2020 | GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz |
| 2020 | Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction. Michael Janner, Igor Mordatch, Sergey Levine |
| 2020 | Gaussian Gated Linear Networks. David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness |
| 2020 | Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood |
| 2020 | General Control Functions for Causal Effect Estimation from IVs. Aahlad Manas Puli, Rajesh Ranganath |
| 2020 | General Transportability of Soft Interventions: Completeness Results. Juan D. Correa, Elias Bareinboim |
| 2020 | Generalised Bayesian Filtering via Sequential Monte Carlo. Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen |
| 2020 | Generalization Bound of Gradient Descent for Non-Convex Metric Learning. Mingzhi Dong, Xiaochen Yang, Rui Zhu, Yujiang Wang, Jing-Hao Xue |
| 2020 | Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics. Taiji Suzuki |
| 2020 | Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. Benjamin Aubin, Florent Krzakala, Yue M. Lu, Lenka Zdeborová |
| 2020 | Generalized Boosting. Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar |
| 2020 | Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang |
| 2020 | Generalized Hindsight for Reinforcement Learning. Alexander C. Li, Lerrel Pinto, Pieter Abbeel |
| 2020 | Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang |
| 2020 | Generalized Leverage Score Sampling for Neural Networks. Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu |
| 2020 | Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning. Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen |
| 2020 | Generating Correct Answers for Progressive Matrices Intelligence Tests. Niv Pekar, Yaniv Benny, Lior Wolf |
| 2020 | Generative 3D Part Assembly via Dynamic Graph Learning. Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong |
| 2020 | Generative Neurosymbolic Machines. Jindong Jiang, Sungjin Ahn |
| 2020 | Generative View Synthesis: From Single-view Semantics to Novel-view Images. Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker |
| 2020 | Generative causal explanations of black-box classifiers. Matthew R. O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark A. Davenport |
| 2020 | Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction. Tong He, John P. Collomosse, Hailin Jin, Stefano Soatto |
| 2020 | Geometric All-way Boolean Tensor Decomposition. Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang |
| 2020 | Geometric Dataset Distances via Optimal Transport. David Alvarez-Melis, Nicolò Fusi |
| 2020 | Geometric Exploration for Online Control. Orestis Plevrakis, Elad Hazan |
| 2020 | Gibbs Sampling with People. Peter M. C. Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby |
| 2020 | Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification. Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang |
| 2020 | Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. Junchi Yang, Negar Kiyavash, Niao He |
| 2020 | Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology. Quynh Nguyen, Marco Mondelli |
| 2020 | Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search. Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon |
| 2020 | Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data. Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang |
| 2020 | Goal-directed Generation of Discrete Structures with Conditional Generative Models. Amina Mollaysa, Brooks Paige, Alexandros Kalousis |
| 2020 | GradAug: A New Regularization Method for Deep Neural Networks. Taojiannan Yang, Sijie Zhu, Chen Chen |
| 2020 | Gradient Boosted Normalizing Flows. Robert A. Giaquinto, Arindam Banerjee |
| 2020 | Gradient Estimation with Stochastic Softmax Tricks. Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison |
| 2020 | Gradient Regularized V-Learning for Dynamic Treatment Regimes. Yao Zhang, Mihaela van der Schaar |
| 2020 | Gradient Surgery for Multi-Task Learning. Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn |
| 2020 | Gradient-EM Bayesian Meta-Learning. Yayi Zou, Xiaoqi Lu |
| 2020 | Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning. Runzhong Wang, Junchi Yan, Xiaokang Yang |
| 2020 | GramGAN: Deep 3D Texture Synthesis From 2D Exemplars. Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel |
| 2020 | Graph Contrastive Learning with Augmentations. Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen |
| 2020 | Graph Cross Networks with Vertex Infomax Pooling. Maosen Li, Siheng Chen, Ya Zhang, Ivor W. Tsang |
| 2020 | Graph Geometry Interaction Learning. Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang |
| 2020 | Graph Information Bottleneck. Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec |
| 2020 | Graph Meta Learning via Local Subgraphs. Kexin Huang, Marinka Zitnik |
| 2020 | Graph Policy Network for Transferable Active Learning on Graphs. Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang |
| 2020 | Graph Random Neural Networks for Semi-Supervised Learning on Graphs. Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang |
| 2020 | Graph Stochastic Neural Networks for Semi-supervised Learning. Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang |
| 2020 | Graphon Neural Networks and the Transferability of Graph Neural Networks. Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro |
| 2020 | Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps. Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, Kui Jia |
| 2020 | Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. Mao Ye, Lemeng Wu, Qiang Liu |
| 2020 | Greedy inference with structure-exploiting lazy maps. Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef M. Marzouk |
| 2020 | GreedyFool: Distortion-Aware Sparse Adversarial Attack. Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen |
| 2020 | Group Contextual Encoding for 3D Point Clouds. Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He |
| 2020 | Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. Chaoyang He, Murali Annavaram, Salman Avestimehr |
| 2020 | Group-Fair Online Allocation in Continuous Time. Semih Cayci, Swati Gupta, Atilla Eryilmaz |
| 2020 | Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses. Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R. |
| 2020 | Guiding Deep Molecular Optimization with Genetic Exploration. Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin |
| 2020 | H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks. Thomas Limbacher, Robert Legenstein |
| 2020 | HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks. Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer |
| 2020 | HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory. Jie Ren, Minjia Zhang, Dong Li |
| 2020 | HOI Analysis: Integrating and Decomposing Human-Object Interaction. Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu |
| 2020 | HRN: A Holistic Approach to One Class Learning. Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu |
| 2020 | HYDRA: Pruning Adversarially Robust Neural Networks. Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana |
| 2020 | Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. Charles C. Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal |
| 2020 | Handling Missing Data with Graph Representation Learning. Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel J. Kochenderfer, Jure Leskovec |
| 2020 | Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning. Huan Fu, Shunming Li, Rongfei Jia, Mingming Gong, Binqiang Zhao, Dacheng Tao |
| 2020 | Hard Negative Mixing for Contrastive Learning. Yannis Kalantidis, Mert Bülent Sariyildiz, Noé Pion, Philippe Weinzaepfel, Diane Larlus |
| 2020 | Hard Shape-Constrained Kernel Machines. Pierre-Cyril Aubin-Frankowski, Zoltán Szabó |
| 2020 | Hardness of Learning Neural Networks with Natural Weights. Amit Daniely, Gal Vardi |
| 2020 | Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu |
| 2020 | Heavy-tailed Representations, Text Polarity Classification & Data Augmentation. Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Éric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin |
| 2020 | Hedging in games: Faster convergence of external and swap regrets. Xi Chen, Binghui Peng |
| 2020 | Heuristic Domain Adaptation. Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang |
| 2020 | HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis. Jungil Kong, Jaehyeon Kim, Jaekyoung Bae |
| 2020 | HiPPO: Recurrent Memory with Optimal Polynomial Projections. Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré |
| 2020 | Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. Theofanis Karaletsos, Thang D. Bui |
| 2020 | Hierarchical Granularity Transfer Learning. Shaobo Min, Hongtao Xie, Hantao Yao, Xuran Deng, Zheng-Jun Zha, Yongdong Zhang |
| 2020 | Hierarchical Neural Architecture Search for Deep Stereo Matching. Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, Zongyuan Ge |
| 2020 | Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample. Shir Gur, Sagie Benaim, Lior Wolf |
| 2020 | Hierarchical Poset Decoding for Compositional Generalization in Language. Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang |
| 2020 | Hierarchical Quantized Autoencoders. Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes |
| 2020 | Hierarchical nucleation in deep neural networks. Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio |
| 2020 | Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems. Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer |
| 2020 | High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. Noémie Jaquier, Leonel Dario Rozo |
| 2020 | High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy |
| 2020 | High-Dimensional Sparse Linear Bandits. Botao Hao, Tor Lattimore, Mengdi Wang |
| 2020 | High-Fidelity Generative Image Compression. Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson |
| 2020 | High-Throughput Synchronous Deep RL. Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing |
| 2020 | High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. Benjamin Cowley, Jonathan W. Pillow |
| 2020 | High-recall causal discovery for autocorrelated time series with latent confounders. Andreas Gerhardus, Jakob Runge |
| 2020 | Higher-Order Certification For Randomized Smoothing. Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel |
| 2020 | Higher-Order Spectral Clustering of Directed Graphs. Steinar Laenen, He Sun |
| 2020 | Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics. Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein |
| 2020 | Hold me tight! Influence of discriminative features on deep network boundaries. Guillermo Ortiz-Jiménez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard |
| 2020 | How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods. Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani B. Srivastava |
| 2020 | How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal |
| 2020 | How do fair decisions fare in long-term qualification? Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang |
| 2020 | How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions. Michael Tsang, Sirisha Rambhatla, Yan Liu |
| 2020 | How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe, Xiaowei Huang |
| 2020 | How hard is to distinguish graphs with graph neural networks? Andreas Loukas |
| 2020 | How many samples is a good initial point worth in Low-rank Matrix Recovery? Jialun Zhang, Richard Y. Zhang |
| 2020 | How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang |
| 2020 | How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization. Pierluca D'Oro, Wojciech Jaskowski |
| 2020 | Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency. Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao |
| 2020 | HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss. Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk |
| 2020 | Hybrid Models for Learning to Branch. Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio |
| 2020 | Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function. Quoc Tran-Dinh, Deyi Liu, Lam M. Nguyen |
| 2020 | Hyperparameter Ensembles for Robustness and Uncertainty Quantification. Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton |
| 2020 | Hypersolvers: Toward Fast Continuous-Depth Models. Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park |
| 2020 | ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping. Cher Bass, Mariana da Silva, Carole H. Sudre, Petru-Daniel Tudosiu, Stephen M. Smith, Emma C. Robinson |
| 2020 | ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen |
| 2020 | ICNet: Intra-saliency Correlation Network for Co-Saliency Detection. Wenda Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo |
| 2020 | IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin |
| 2020 | ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding. Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, Zhouchen Lin |
| 2020 | Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal |
| 2020 | Identifying Learning Rules From Neural Network Observables. Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins |
| 2020 | Identifying Mislabeled Data using the Area Under the Margin Ranking. Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger |
| 2020 | Identifying signal and noise structure in neural population activity with Gaussian process factor models. Stephen L. Keeley, Mikio C. Aoi, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow |
| 2020 | ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier |
| 2020 | Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry |
| 2020 | Implicit Distributional Reinforcement Learning. Yuguang Yue, Zhendong Wang, Mingyuan Zhou |
| 2020 | Implicit Graph Neural Networks. Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui |
| 2020 | Implicit Neural Representations with Periodic Activation Functions. Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein |
| 2020 | Implicit Rank-Minimizing Autoencoder. Li Jing, Jure Zbontar, Yann LeCun |
| 2020 | Implicit Regularization and Convergence for Weight Normalization. Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu |
| 2020 | Implicit Regularization in Deep Learning May Not Be Explainable by Norms. Noam Razin, Nadav Cohen |
| 2020 | Impossibility Results for Grammar-Compressed Linear Algebra. Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann |
| 2020 | Improved Algorithms for Convex-Concave Minimax Optimization. Yuanhao Wang, Jian Li |
| 2020 | Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma |
| 2020 | Improved Analysis of Clipping Algorithms for Non-convex Optimization. Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang |
| 2020 | Improved Guarantees for k-means++ and k-means++ Parallel. Konstantin Makarychev, Aravind Reddy, Liren Shan |
| 2020 | Improved Sample Complexity for Incremental Autonomous Exploration in MDPs. Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric |
| 2020 | Improved Schemes for Episodic Memory-based Lifelong Learning. Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing |
| 2020 | Improved Techniques for Training Score-Based Generative Models. Yang Song, Stefano Ermon |
| 2020 | Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows. Cheng Zhang |
| 2020 | Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. Michal Derezinski, Rajiv Khanna, Michael W. Mahoney |
| 2020 | Improving Auto-Augment via Augmentation-Wise Weight Sharing. Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, Wanli Ouyang |
| 2020 | Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu |
| 2020 | Improving Generalization in Reinforcement Learning with Mixture Regularization. Kaixin Wang, Bingyi Kang, Jie Shao, Jiashi Feng |
| 2020 | Improving Inference for Neural Image Compression. Yibo Yang, Robert Bamler, Stephan Mandt |
| 2020 | Improving Local Identifiability in Probabilistic Box Embeddings. Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum |
| 2020 | Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention. Ekta Sood, Simon Tannert, Philipp Müller, Andreas Bulling |
| 2020 | Improving Neural Network Training in Low Dimensional Random Bases. Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi |
| 2020 | Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions. Soumya Banerjee |
| 2020 | Improving Policy-Constrained Kidney Exchange via Pre-Screening. Duncan C. McElfresh, Michael J. Curry, Tuomas Sandholm, John Dickerson |
| 2020 | Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. Tengyu Xu, Zhe Wang, Yingbin Liang |
| 2020 | Improving Sparse Vector Technique with Renyi Differential Privacy. Yuqing Zhu, Yu-Xiang Wang |
| 2020 | Improving model calibration with accuracy versus uncertainty optimization. Ranganath Krishnan, Omesh Tickoo |
| 2020 | Improving robustness against common corruptions by covariate shift adaptation. Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge |
| 2020 | In search of robust measures of generalization. Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy |
| 2020 | Incorporating BERT into Parallel Sequence Decoding with Adapters. Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen, Enhong Chen |
| 2020 | Incorporating Interpretable Output Constraints in Bayesian Neural Networks. Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez |
| 2020 | Incorporating Pragmatic Reasoning Communication into Emergent Language. Yipeng Kang, Tonghan Wang, Gerard de Melo |
| 2020 | Independent Policy Gradient Methods for Competitive Reinforcement Learning. Constantinos Daskalakis, Dylan J. Foster, Noah Golowich |
| 2020 | Inductive Quantum Embedding. Santosh K. Srivastava, Dinesh Khandelwal, Dhiraj Madan, Dinesh Garg, Hima Karanam, L. Venkata Subramaniam |
| 2020 | Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation. Jianfeng Zhang, Xuecheng Nie, Jiashi Feng |
| 2020 | Inference for Batched Bandits. Kelly W. Zhang, Lucas Janson, Susan A. Murphy |
| 2020 | Inferring learning rules from animal decision-making. Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow |
| 2020 | Influence-Augmented Online Planning for Complex Environments. Jinke He, Miguel Suau, Frans A. Oliehoek |
| 2020 | Information Maximization for Few-Shot Learning. Malik Boudiaf, Imtiaz Masud Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed |
| 2020 | Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback. Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng |
| 2020 | Information Theoretic Regret Bounds for Online Nonlinear Control. Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun |
| 2020 | Information theoretic limits of learning a sparse rule. Clément Luneau, Jean Barbier, Nicolas Macris |
| 2020 | Information-theoretic Task Selection for Meta-Reinforcement Learning. Ricardo Luna Gutierrez, Matteo Leonetti |
| 2020 | Input-Aware Dynamic Backdoor Attack. Tuan Anh Nguyen, Anh Tuan Tran |
| 2020 | Instance Based Approximations to Profile Maximum Likelihood. Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford |
| 2020 | Instance Selection for GANs. Terrance DeVries, Michal Drozdzal, Graham W. Taylor |
| 2020 | Instance-based Generalization in Reinforcement Learning. Martín Bertrán, Natalia Martínez, Mariano Phielipp, Guillermo Sapiro |
| 2020 | Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Hilal Asi, John C. Duchi |
| 2020 | Instance-wise Feature Grouping. Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter J. Castaldi, Jennifer G. Dy |
| 2020 | Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients. William S. Moses, Valentin Churavy |
| 2020 | Interferobot: aligning an optical interferometer by a reinforcement learning agent. Dmitry Igorevich Sorokin, Alexander E. Ulanov, Ekaterina A. Sazhina, Alexander I. Lvovsky |
| 2020 | Interior Point Solving for LP-based prediction+optimisation. Jayanta Mandi, Tias Guns |
| 2020 | Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan V. Oseledets |
| 2020 | Interpretable Sequence Learning for Covid-19 Forecasting. Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister |
| 2020 | Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations. Rohan R. Paleja, Andrew Silva, Letian Chen, Matthew C. Gombolay |
| 2020 | Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech. Shailee Jain, Vy A. Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth |
| 2020 | Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. Yongqi Zhang, Quanming Yao, Lei Chen |
| 2020 | Interventional Few-Shot Learning. Zhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua |
| 2020 | Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots |
| 2020 | Intra-Processing Methods for Debiasing Neural Networks. Yash Savani, Colin White, Naveen Sundar Govindarajulu |
| 2020 | Introducing Routing Uncertainty in Capsule Networks. Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos D. Kollias |
| 2020 | Inverse Learning of Symmetries. Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth |
| 2020 | Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics. Minhae Kwon, Saurabh Daptardar, Paul R. Schrater, Xaq Pitkow |
| 2020 | Inverse Reinforcement Learning from a Gradient-based Learner. Giorgia Ramponi, Gianluca Drappo, Marcello Restelli |
| 2020 | Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham |
| 2020 | Inverting Gradients - How easy is it to break privacy in federated learning? Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller |
| 2020 | Investigating Gender Bias in Language Models Using Causal Mediation Analysis. Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart M. Shieber |
| 2020 | Is Long Horizon RL More Difficult Than Short Horizon RL? Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade |
| 2020 | Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? Qiwen Cui, Lin F. Yang |
| 2020 | Is normalization indispensable for training deep neural network? Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj |
| 2020 | Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. Yu Chen, Lingfei Wu, Mohammed J. Zaki |
| 2020 | JAX MD: A Framework for Differentiable Physics. Samuel S. Schoenholz, Ekin Dogus Cubuk |
| 2020 | Joint Contrastive Learning with Infinite Possibilities. Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei |
| 2020 | Joint Policy Search for Multi-agent Collaboration with Imperfect Information. Yuandong Tian, Qucheng Gong, Yu Jiang |
| 2020 | Joints in Random Forests. Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos |
| 2020 | Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout. Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov |
| 2020 | KFC: A Scalable Approximation Algorithm for $k$-center Fair Clustering. Elfarouk Harb, Ho Shan Lam |
| 2020 | Kalman Filtering Attention for User Behavior Modeling in CTR Prediction. Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan |
| 2020 | Kernel Alignment Risk Estimator: Risk Prediction from Training Data. Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel |
| 2020 | Kernel Based Progressive Distillation for Adder Neural Networks. Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing Xu, Yunhe Wang |
| 2020 | Kernel Methods Through the Roof: Handling Billions of Points Efficiently. Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi |
| 2020 | Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. Roman Pogodin, Peter E. Latham |
| 2020 | Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition. Zijun Cui, Tengfei Song, Yuru wang, Qiang Ji |
| 2020 | Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. Guangda Ji, Zhanxing Zhu |
| 2020 | Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control. Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye |
| 2020 | LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond. Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia |
| 2020 | Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity. Shuxiao Chen, Hangfeng He, Weijie J. Su |
| 2020 | Labelling unlabelled videos from scratch with multi-modal self-supervision. Yuki Markus Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi |
| 2020 | Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks. Dongqi Han, Erik De Schutter, Sungho Hong |
| 2020 | Language Models are Few-Shot Learners. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei |
| 2020 | Language Through a Prism: A Spectral Approach for Multiscale Language Representations. Alex Tamkin, Dan Jurafsky, Noah D. Goodman |
| 2020 | Language and Visual Entity Relationship Graph for Agent Navigation. Yicong Hong, Cristian Rodriguez Opazo, Yuankai Qi, Qi Wu, Stephen Gould |
| 2020 | Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration. Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter F. Dominey, Pierre-Yves Oudeyer |
| 2020 | Language-Conditioned Imitation Learning for Robot Manipulation Tasks. Simon Stepputtis, Joseph Campbell, Mariano J. Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor |
| 2020 | Large-Scale Adversarial Training for Vision-and-Language Representation Learning. Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu |
| 2020 | Large-Scale Methods for Distributionally Robust Optimization. Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford |
| 2020 | Latent Bandits Revisited. Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier |
| 2020 | Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings. Heejong Bong, Zongge Liu, Zhao Ren, Matthew A. Smith, Valérie Ventura, Robert E. Kass |
| 2020 | Latent Template Induction with Gumbel-CRFs. Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander M. Rush |
| 2020 | Latent World Models For Intrinsically Motivated Exploration. Aleksandr Ermolov, Nicu Sebe |
| 2020 | Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge. Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant |
| 2020 | Learnability with Indirect Supervision Signals. Kaifu Wang, Qiang Ning, Dan Roth |
| 2020 | Learning About Objects by Learning to Interact with Them. Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi |
| 2020 | Learning Affordance Landscapes for Interaction Exploration in 3D Environments. Tushar Nagarajan, Kristen Grauman |
| 2020 | Learning Agent Representations for Ice Hockey. Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan |
| 2020 | Learning Augmented Energy Minimization via Speed Scaling. Étienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson |
| 2020 | Learning Black-Box Attackers with Transferable Priors and Query Feedback. Jiancheng Yang, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao |
| 2020 | Learning Bounds for Risk-sensitive Learning. Jaeho Lee, Sejun Park, Jinwoo Shin |
| 2020 | Learning Causal Effects via Weighted Empirical Risk Minimization. Yonghan Jung, Jin Tian, Elias Bareinboim |
| 2020 | Learning Certified Individually Fair Representations. Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev |
| 2020 | Learning Composable Energy Surrogates for PDE Order Reduction. Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams |
| 2020 | Learning Compositional Rules via Neural Program Synthesis. Maxwell I. Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M. Lake |
| 2020 | Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations. Zijie Huang, Yizhou Sun, Wei Wang |
| 2020 | Learning Deep Attribution Priors Based On Prior Knowledge. Ethan Weinberger, Joseph D. Janizek, Su-In Lee |
| 2020 | Learning Deformable Tetrahedral Meshes for 3D Reconstruction. Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, Sanja Fidler |
| 2020 | Learning Differentiable Programs with Admissible Neural Heuristics. Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri |
| 2020 | Learning Differential Equations that are Easy to Solve. Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud |
| 2020 | Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans |
| 2020 | Learning Disentangled Representations and Group Structure of Dynamical Environments. Robin Quessard, Thomas D. Barrett, William R. Clements |
| 2020 | Learning Disentangled Representations of Videos with Missing Data. Armand Comas Massague, Chi Zhang, Zlatan Feric, Octavia I. Camps, Rose Yu |
| 2020 | Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction. Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma |
| 2020 | Learning Dynamic Belief Graphs to Generalize on Text-Based Games. Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L. Hamilton |
| 2020 | Learning Feature Sparse Principal Subspace. Lai Tian, Feiping Nie, Rong Wang, Xuelong Li |
| 2020 | Learning Global Transparent Models consistent with Local Contrastive Explanations. Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar |
| 2020 | Learning Graph Structure With A Finite-State Automaton Layer. Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow |
| 2020 | Learning Guidance Rewards with Trajectory-space Smoothing. Tanmay Gangwani, Yuan Zhou, Jian Peng |
| 2020 | Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning. Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung |
| 2020 | Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence. Feng Liu, Xiaoming Liu |
| 2020 | Learning Individually Inferred Communication for Multi-Agent Cooperation. Ziluo Ding, Tiejun Huang, Zongqing Lu |
| 2020 | Learning Invariances in Neural Networks from Training Data. Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson |
| 2020 | Learning Invariants through Soft Unification. Nuri Cingillioglu, Alessandra Russo |
| 2020 | Learning Kernel Tests Without Data Splitting. Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet |
| 2020 | Learning Latent Space Energy-Based Prior Model. Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu |
| 2020 | Learning Linear Programs from Optimal Decisions. Yingcong Tan, Daria Terekhov, Andrew Delong |
| 2020 | Learning Loss for Test-Time Augmentation. Ildoo Kim, Younghoon Kim, Sungwoong Kim |
| 2020 | Learning Manifold Implicitly via Explicit Heat-Kernel Learning. Yufan Zhou, Changyou Chen, Jinhui Xu |
| 2020 | Learning Multi-Agent Communication through Structured Attentive Reasoning. Murtaza Rangwala, Ryan Williams |
| 2020 | Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks. Jing Xu, Fangwei Zhong, Yizhou Wang |
| 2020 | Learning Mutational Semantics. Brian Hie, Ellen D. Zhong, Bryan Bryson, Bonnie Berger |
| 2020 | Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views. Nanbo Li, Cian Eastwood, Robert B. Fisher |
| 2020 | Learning Optimal Representations with the Decodable Information Bottleneck. Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam |
| 2020 | Learning Parities with Neural Networks. Amit Daniely, Eran Malach |
| 2020 | Learning Physical Constraints with Neural Projections. Shuqi Yang, Xingzhe He, Bo Zhu |
| 2020 | Learning Physical Graph Representations from Visual Scenes. Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. K. Yamins |
| 2020 | Learning Representations from Audio-Visual Spatial Alignment. Pedro Morgado, Yi Li, Nuno Vasconcelos |
| 2020 | Learning Restricted Boltzmann Machines with Sparse Latent Variables. Guy Bresler, Rares-Darius Buhai |
| 2020 | Learning Retrospective Knowledge with Reverse Reinforcement Learning. Shangtong Zhang, Vivek Veeriah, Shimon Whiteson |
| 2020 | Learning Rich Rankings. Arjun Seshadri, Stephen Ragain, Johan Ugander |
| 2020 | Learning Robust Decision Policies from Observational Data. Muhammad Osama, Dave Zachariah, Peter Stoica |
| 2020 | Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. Linnan Wang, Rodrigo Fonseca, Yuandong Tian |
| 2020 | Learning Semantic-aware Normalization for Generative Adversarial Networks. Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha |
| 2020 | Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra |
| 2020 | Learning Sparse Prototypes for Text Generation. Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig |
| 2020 | Learning Strategic Network Emergence Games. Rakshit S. Trivedi, Hongyuan Zha |
| 2020 | Learning Strategy-Aware Linear Classifiers. Yiling Chen, Yang Liu, Chara Podimata |
| 2020 | Learning Structured Distributions From Untrusted Batches: Faster and Simpler. Sitan Chen, Jerry Li, Ankur Moitra |
| 2020 | Learning Utilities and Equilibria in Non-Truthful Auctions. Hu Fu, Tao Lin |
| 2020 | Learning abstract structure for drawing by efficient motor program induction. Lucas Yanan Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum |
| 2020 | Learning by Minimizing the Sum of Ranked Range. Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu |
| 2020 | Learning compositional functions via multiplicative weight updates. Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue |
| 2020 | Learning discrete distributions with infinite support. Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer |
| 2020 | Learning discrete distributions: user vs item-level privacy. Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley |
| 2020 | Learning efficient task-dependent representations with synaptic plasticity. Colin Bredenberg, Eero P. Simoncelli, Cristina Savin |
| 2020 | Learning from Aggregate Observations. Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama |
| 2020 | Learning from Failure: De-biasing Classifier from Biased Classifier. Jun Hyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, Jinwoo Shin |
| 2020 | Learning from Label Proportions: A Mutual Contamination Framework. Clayton Scott, Jianxin Zhang |
| 2020 | Learning from Mixtures of Private and Public Populations. Raef Bassily, Shay Moran, Anupama Nandi |
| 2020 | Learning from Positive and Unlabeled Data with Arbitrary Positive Shift. Zayd Hammoudeh, Daniel Lowd |
| 2020 | Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE. Ding Zhou, Xue-Xin Wei |
| 2020 | Learning of Discrete Graphical Models with Neural Networks. Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray |
| 2020 | Learning outside the Black-Box: The pursuit of interpretable models. Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar |
| 2020 | Learning sparse codes from compressed representations with biologically plausible local wiring constraints. Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell |
| 2020 | Learning the Geometry of Wave-Based Imaging. Konik Kothari, Maarten V. de Hoop, Ivan Dokmanic |
| 2020 | Learning the Linear Quadratic Regulator from Nonlinear Observations. Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford |
| 2020 | Learning to Adapt to Evolving Domains. Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang |
| 2020 | Learning to Approximate a Bregman Divergence. Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis |
| 2020 | Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes. Salman Habib, Allison Beemer, Jörg Kliewer |
| 2020 | Learning to Detect Objects with a 1 Megapixel Event Camera. Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, Amos Sironi |
| 2020 | Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu |
| 2020 | Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow |
| 2020 | Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction. Jinheon Baek, Dong Bok Lee, Sung Ju Hwang |
| 2020 | Learning to Incentivize Other Learning Agents. Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha |
| 2020 | Learning to Learn Variational Semantic Memory. Xiantong Zhen, Ying-Jun Du, Huan Xiong, Qiang Qiu, Cees Snoek, Ling Shao |
| 2020 | Learning to Learn with Feedback and Local Plasticity. Jack Lindsey, Ashok Litwin-Kumar |
| 2020 | Learning to Mutate with Hypergradient Guided Population. Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu |
| 2020 | Learning to Orient Surfaces by Self-supervised Spherical CNNs. Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano |
| 2020 | Learning to Play No-Press Diplomacy with Best Response Policy Iteration. Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach |
| 2020 | Learning to Play Sequential Games versus Unknown Opponents. Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause |
| 2020 | Learning to Prove Theorems by Learning to Generate Theorems. Mingzhe Wang, Jia Deng |
| 2020 | Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai |
| 2020 | Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping. Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, Changjie Fan |
| 2020 | Learning to search efficiently for causally near-optimal treatments. Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson |
| 2020 | Learning to solve TV regularised problems with unrolled algorithms. Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau |
| 2020 | Learning to summarize with human feedback. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano |
| 2020 | Learning under Model Misspecification: Applications to Variational and Ensemble methods. Andrés R. Masegosa |
| 2020 | Learning with Differentiable Pertubed Optimizers. Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach |
| 2020 | Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces. Akash Saha, P. Balamurugan |
| 2020 | Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions. Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi |
| 2020 | Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli |
| 2020 | Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist |
| 2020 | Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms. Yingying Li, Na Li |
| 2020 | Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. Sebastian Farquhar, Lewis Smith, Yarin Gal |
| 2020 | Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting. Jorge A. Mendez, Boyu Wang, Eric Eaton |
| 2020 | Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation. Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz |
| 2020 | Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. Zhisheng Xiao, Qing Yan, Yali Amit |
| 2020 | Limits on Testing Structural Changes in Ising Models. Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama |
| 2020 | Limits to Depth Efficiencies of Self-Attention. Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua |
| 2020 | Linear Disentangled Representations and Unsupervised Action Estimation. Matthew Painter, Adam Prügel-Bennett, Jonathon S. Hare |
| 2020 | Linear Dynamical Systems as a Core Computational Primitive. Shiva Kaul |
| 2020 | Linear Time Sinkhorn Divergences using Positive Features. Meyer Scetbon, Marco Cuturi |
| 2020 | Linear-Sample Learning of Low-Rank Distributions. Ayush Jain, Alon Orlitsky |
| 2020 | Linearly Converging Error Compensated SGD. Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtárik |
| 2020 | Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing. Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti |
| 2020 | Lipschitz-Certifiable Training with a Tight Outer Bound. Sungyoon Lee, Jaewook Lee, Saerom Park |
| 2020 | List-Decodable Mean Estimation via Iterative Multi-Filtering. Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard |
| 2020 | Listening to Sounds of Silence for Speech Denoising. Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng |
| 2020 | LoCo: Local Contrastive Representation Learning. Yuwen Xiong, Mengye Ren, Raquel Urtasun |
| 2020 | Locally Differentially Private (Contextual) Bandits Learning. Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang |
| 2020 | Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms. Thomas Berrett, Cristina Butucea |
| 2020 | Locally-Adaptive Nonparametric Online Learning. Ilja Kuzborskij, Nicolò Cesa-Bianchi |
| 2020 | Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment. Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko |
| 2020 | Logarithmic Pruning is All You Need. Laurent Orseau, Marcus Hutter, Omar Rivasplata |
| 2020 | Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar |
| 2020 | Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine |
| 2020 | Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. Kaihua Tang, Jianqiang Huang, Hanwang Zhang |
| 2020 | Look-ahead Meta Learning for Continual Learning. Gunshi Gupta, Karmesh Yadav, Liam Paull |
| 2020 | LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration. Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll |
| 2020 | Low Distortion Block-Resampling with Spatially Stochastic Networks. Sarah Jane Hong, Martín Arjovsky, Darryl Barnhart, Ian Thompson |
| 2020 | Lower Bounds and Optimal Algorithms for Personalized Federated Learning. Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik |
| 2020 | MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet |
| 2020 | MCUNet: Tiny Deep Learning on IoT Devices. Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han |
| 2020 | MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling |
| 2020 | MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler. Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang |
| 2020 | MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles. Zhennan Wang, Canqun Xiang, Wenbin Zou, Chen Xu |
| 2020 | MOPO: Model-based Offline Policy Optimization. Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma |
| 2020 | MOReL: Model-Based Offline Reinforcement Learning. Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims |
| 2020 | MPNet: Masked and Permuted Pre-training for Language Understanding. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu |
| 2020 | MRI Banding Removal via Adversarial Training. Aaron Defazio, Tullie Murrell, Michael P. Recht |
| 2020 | Make One-Shot Video Object Segmentation Efficient Again. Tim Meinhardt, Laura Leal-Taixé |
| 2020 | Making Non-Stochastic Control (Almost) as Easy as Stochastic. Max Simchowitz |
| 2020 | Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin |
| 2020 | Manifold structure in graph embeddings. Patrick Rubin-Delanchy |
| 2020 | Marginal Utility for Planning in Continuous or Large Discrete Action Spaces. Zaheen Farraz Ahmad, Levi Lelis, Michael Bowling |
| 2020 | Margins are Insufficient for Explaining Gradient Boosting. Allan Grønlund, Lior Kamma, Kasper Green Larsen |
| 2020 | Markovian Score Climbing: Variational Inference with KL(p||q). Christian A. Naesseth, Fredrik Lindsten, David M. Blei |
| 2020 | Matrix Completion with Hierarchical Graph Side Information. Adel M. Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer |
| 2020 | Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula. Yuxuan Zhao, Madeleine Udell |
| 2020 | Matrix Inference and Estimation in Multi-Layer Models. Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher |
| 2020 | Matérn Gaussian Processes on Riemannian Manifolds. Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth |
| 2020 | Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness. Long Zhao, Ting Liu, Xi Peng, Dimitris N. Metaxas |
| 2020 | Measuring Robustness to Natural Distribution Shifts in Image Classification. Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt |
| 2020 | Measuring Systematic Generalization in Neural Proof Generation with Transformers. Nicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher Pal |
| 2020 | Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards. Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee |
| 2020 | Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control. Giorgos Mamakoukas, Orest Xherija, Todd D. Murphey |
| 2020 | MeshSDF: Differentiable Iso-Surface Extraction. Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur M. Bagautdinov, Pierre Baqué, Pascal Fua |
| 2020 | Meta-Consolidation for Continual Learning. K. J. Joseph, Vineeth Nallure Balasubramanian |
| 2020 | Meta-Gradient Reinforcement Learning with an Objective Discovered Online. Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver |
| 2020 | Meta-Learning Requires Meta-Augmentation. Janarthanan Rajendran, Alexander Irpan, Eric Jang |
| 2020 | Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner |
| 2020 | Meta-Learning through Hebbian Plasticity in Random Networks. Elias Najarro, Sebastian Risi |
| 2020 | Meta-Learning with Adaptive Hyperparameters. Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee |
| 2020 | Meta-Neighborhoods. Siyuan Shan, Yang Li, Junier B. Oliva |
| 2020 | Meta-learning from Tasks with Heterogeneous Attribute Spaces. Tomoharu Iwata, Atsutoshi Kumagai |
| 2020 | Meta-trained agents implement Bayes-optimal agents. Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega |
| 2020 | MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures. Jeongun Ryu, Jaewoong Shin, Haebeom Lee, Sung Ju Hwang |
| 2020 | MetaPoison: Practical General-purpose Clean-label Data Poisoning. W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein |
| 2020 | MetaSDF: Meta-Learning Signed Distance Functions. Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein |
| 2020 | Metric-Free Individual Fairness in Online Learning. Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu |
| 2020 | MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics. Luca De Gennaro Aquino, Stephan Eckstein |
| 2020 | MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou |
| 2020 | Minibatch Stochastic Approximate Proximal Point Methods. Hilal Asi, Karan N. Chadha, Gary Cheng, John C. Duchi |
| 2020 | Minibatch vs Local SGD for Heterogeneous Distributed Learning. Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro |
| 2020 | Minimax Bounds for Generalized Linear Models. Kuan-Yun Lee, Thomas A. Courtade |
| 2020 | Minimax Classification with 0-1 Loss and Performance Guarantees. Santiago Mazuelas, Andrea Zanoni, Aritz Pérez |
| 2020 | Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons. Qianyi Li, Cengiz Pehlevan |
| 2020 | Minimax Estimation of Conditional Moment Models. Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis |
| 2020 | Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks. Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi |
| 2020 | Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects. Zijun Gao, Yanjun Han |
| 2020 | Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition. Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi |
| 2020 | Minimax Value Interval for Off-Policy Evaluation and Policy Optimization. Nan Jiang, Jiawei Huang |
| 2020 | Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization. Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun |
| 2020 | Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments. Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, Fei Fang |
| 2020 | Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai |
| 2020 | Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables. Guangyao Zhou |
| 2020 | Model Agnostic Multilevel Explanations. Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar |
| 2020 | Model Class Reliance for Random Forests. Gavin Smith, Roberto Mansilla, James Goulding |
| 2020 | Model Fusion via Optimal Transport. Sidak Pal Singh, Martin Jaggi |
| 2020 | Model Interpretability through the lens of Computational Complexity. Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux |
| 2020 | Model Inversion Networks for Model-Based Optimization. Aviral Kumar, Sergey Levine |
| 2020 | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang |
| 2020 | Model Selection for Production System via Automated Online Experiments. Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin A. Carterette, Mounia Lalmas |
| 2020 | Model Selection in Contextual Stochastic Bandit Problems. Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári |
| 2020 | Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity. Kaiqing Zhang, Sham M. Kakade, Tamer Basar, Lin F. Yang |
| 2020 | Model-based Adversarial Meta-Reinforcement Learning. Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma |
| 2020 | Model-based Policy Optimization with Unsupervised Model Adaptation. Jian Shen, Han Zhao, Weinan Zhang, Yong Yu |
| 2020 | Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. Jianzhun Du, Joseph Futoma, Finale Doshi-Velez |
| 2020 | Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows. Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas M. Lehrmann |
| 2020 | Modeling Noisy Annotations for Crowd Counting. Jia Wan, Antoni B. Chan |
| 2020 | Modeling Shared responses in Neuroimaging Studies through MultiView ICA. Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin |
| 2020 | Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction. Mariya Toneva, Otilia Stretcu, Barnabás Póczos, Leila Wehbe, Tom M. Mitchell |
| 2020 | Modeling and Optimization Trade-off in Meta-learning. Katelyn Gao, Ozan Sener |
| 2020 | Modern Hopfield Networks and Attention for Immune Repertoire Classification. Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer |
| 2020 | Modular Meta-Learning with Shrinkage. Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas |
| 2020 | MomentumRNN: Integrating Momentum into Recurrent Neural Networks. Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang |
| 2020 | Monotone operator equilibrium networks. Ezra Winston, J. Zico Kolter |
| 2020 | Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations. Amit Daniely, Hadas Shacham |
| 2020 | Movement Pruning: Adaptive Sparsity by Fine-Tuning. Victor Sanh, Thomas Wolf, Alexander M. Rush |
| 2020 | MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models. Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun |
| 2020 | Multi-Fidelity Bayesian Optimization via Deep Neural Networks. Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe |
| 2020 | Multi-Plane Program Induction with 3D Box Priors. Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu |
| 2020 | Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates. Wenhao Luo, Wen Sun, Ashish Kapoor |
| 2020 | Multi-Stage Influence Function. Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh |
| 2020 | Multi-Task Reinforcement Learning with Soft Modularization. Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang |
| 2020 | Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement. Xin Liu, Josh Fromm, Shwetak N. Patel, Daniel McDuff |
| 2020 | Multi-agent Trajectory Prediction with Fuzzy Query Attention. Nitin Kamra, Hao Zhu, Dweep Trivedi, Ming Zhang, Yan Liu |
| 2020 | Multi-agent active perception with prediction rewards. Mikko Lauri, Frans A. Oliehoek |
| 2020 | Multi-label Contrastive Predictive Coding. Jiaming Song, Stefano Ermon |
| 2020 | Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? Guoqiang Wu, Jun Zhu |
| 2020 | Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery. Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen |
| 2020 | Multi-task Batch Reinforcement Learning with Metric Learning. Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik I. Christensen, Hao Su |
| 2020 | Multi-task Causal Learning with Gaussian Processes. Virginia Aglietti, Theodoros Damoulas, Mauricio A. Álvarez, Javier González |
| 2020 | MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation. Saim Wani, Shivansh Patel, Unnat Jain, Angel X. Chang, Manolis Savva |
| 2020 | Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu |
| 2020 | Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping. Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Günlük |
| 2020 | Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt |
| 2020 | Multimodal Graph Networks for Compositional Generalization in Visual Question Answering. Raeid Saqur, Karthik Narasimhan |
| 2020 | Multiparameter Persistence Image for Topological Machine Learning. Mathieu Carrière, Andrew J. Blumberg |
| 2020 | Multipole Graph Neural Operator for Parametric Partial Differential Equations. Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar |
| 2020 | Multiscale Deep Equilibrium Models. Shaojie Bai, Vladlen Koltun, J. Zico Kolter |
| 2020 | Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance. Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, Yaron Lipman |
| 2020 | Munchausen Reinforcement Learning. Nino Vieillard, Olivier Pietquin, Matthieu Geist |
| 2020 | Mutual exclusivity as a challenge for deep neural networks. Kanishk Gandhi, Brenden M. Lake |
| 2020 | Myersonian Regression. Allen Liu, Renato Paes Leme, Jon Schneider |
| 2020 | NVAE: A Deep Hierarchical Variational Autoencoder. Arash Vahdat, Jan Kautz |
| 2020 | NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity. Sang-gil Lee, Sungwon Kim, Sungroh Yoon |
| 2020 | Natural Graph Networks. Pim de Haan, Taco S. Cohen, Max Welling |
| 2020 | Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes. Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo R. Jovanovic |
| 2020 | Near-Optimal Comparison Based Clustering. Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar |
| 2020 | Near-Optimal Reinforcement Learning with Self-Play. Yu Bai, Chi Jin, Tiancheng Yu |
| 2020 | Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. Ilias Diakonikolas, Daniel Kane, Nikos Zarifis |
| 2020 | Network Diffusions via Neural Mean-Field Dynamics. Shushan He, Hongyuan Zha, Xiaojing Ye |
| 2020 | Network size and size of the weights in memorization with two-layers neural networks. Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer |
| 2020 | Network-to-Network Translation with Conditional Invertible Neural Networks. Robin Rombach, Patrick Esser, Björn Ommer |
| 2020 | NeuMiss networks: differentiable programming for supervised learning with missing values. Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux |
| 2020 | Neural Anisotropy Directions. Guillermo Ortiz-Jiménez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard |
| 2020 | Neural Architecture Generator Optimization. Robin Ru, Pedro M. Esperança, Fabio Maria Carlucci |
| 2020 | Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi |
| 2020 | Neural Complexity Measures. Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi |
| 2020 | Neural Controlled Differential Equations for Irregular Time Series. Patrick Kidger, James Morrill, James Foster, Terry J. Lyons |
| 2020 | Neural Dynamic Policies for End-to-End Sensorimotor Learning. Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak |
| 2020 | Neural Execution Engines: Learning to Execute Subroutines. Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi |
| 2020 | Neural FFTs for Universal Texture Image Synthesis. Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro |
| 2020 | Neural Manifold Ordinary Differential Equations. Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa |
| 2020 | Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows. Kunal Gupta, Manmohan Chandraker |
| 2020 | Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs. Naganand Yadati |
| 2020 | Neural Methods for Point-wise Dependency Estimation. Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov |
| 2020 | Neural Networks Fail to Learn Periodic Functions and How to Fix It. Liu Ziyin, Tilman Hartwig, Masahito Ueda |
| 2020 | Neural Networks Learning and Memorization with (almost) no Over-Parameterization. Amit Daniely |
| 2020 | Neural Networks with Recurrent Generative Feedback. Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar |
| 2020 | Neural Networks with Small Weights and Depth-Separation Barriers. Gal Vardi, Ohad Shamir |
| 2020 | Neural Non-Rigid Tracking. Aljaz Bozic, Pablo R. Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Nießner |
| 2020 | Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning. Chandrashekar Lakshminarayanan, Amit Vikram Singh |
| 2020 | Neural Power Units. Niklas Heim, Tomás Pevný, Václav Smídl |
| 2020 | Neural Sparse Representation for Image Restoration. Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang |
| 2020 | Neural Sparse Voxel Fields. Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt |
| 2020 | Neural Star Domain as Primitive Representation. Yuki Kawana, Yusuke Mukuta, Tatsuya Harada |
| 2020 | Neural Topographic Factor Analysis for fMRI Data. Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J. Benjamin Hutchinson, Ajay B. Satpute, Jennifer G. Dy, Jan-Willem van de Meent |
| 2020 | Neural Unsigned Distance Fields for Implicit Function Learning. Julian Chibane, Aymen Mir, Gerard Pons-Moll |
| 2020 | Neural encoding with visual attention. Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu |
| 2020 | Neuron Merging: Compensating for Pruned Neurons. Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon |
| 2020 | Neuron Shapley: Discovering the Responsible Neurons. Amirata Ghorbani, James Y. Zou |
| 2020 | Neuron-level Structured Pruning using Polarization Regularizer. Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, Xiang Li |
| 2020 | Neuronal Gaussian Process Regression. Johannes Friedrich |
| 2020 | Neurosymbolic Reinforcement Learning with Formally Verified Exploration. Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri |
| 2020 | Neurosymbolic Transformers for Multi-Agent Communication. Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin C. Rinard, Armando Solar-Lezama |
| 2020 | Neutralizing Self-Selection Bias in Sampling for Sortition. Bailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D. Procaccia |
| 2020 | Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun |
| 2020 | No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems. Nimit Sharad Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré |
| 2020 | No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium. Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti |
| 2020 | No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix. Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras |
| 2020 | No-regret Learning in Price Competitions under Consumer Reference Effects. Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang |
| 2020 | Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding. Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan |
| 2020 | Node Embeddings and Exact Low-Rank Representations of Complex Networks. Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis |
| 2020 | Noise-Contrastive Estimation for Multivariate Point Processes. Hongyuan Mei, Tom Wan, Jason Eisner |
| 2020 | Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising. Yaochen Xie, Zhengyang Wang, Shuiwang Ji |
| 2020 | Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2020 | Non-Crossing Quantile Regression for Distributional Reinforcement Learning. Fan Zhou, Jianing Wang, Xingdong Feng |
| 2020 | Non-Euclidean Universal Approximation. Anastasis Kratsios, Ievgen Bilokopytov |
| 2020 | Non-Stochastic Control with Bandit Feedback. Paula Gradu, John Hallman, Elad Hazan |
| 2020 | Non-parametric Models for Non-negative Functions. Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi |
| 2020 | Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin |
| 2020 | Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors. Jorio Cocola, Paul Hand, Vladislav Voroninski |
| 2020 | Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar |
| 2020 | Normalizing Kalman Filters for Multivariate Time Series Analysis. Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski |
| 2020 | Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing |
| 2020 | Novelty Search in Representational Space for Sample Efficient Exploration. Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau |
| 2020 | Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. Julius Berner, Markus Dablander, Philipp Grohs |
| 2020 | O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar |
| 2020 | OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification. Taewon Jeong, Heeyoung Kim |
| 2020 | OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling. Viet Huynh, He Zhao, Dinh Phung |
| 2020 | Object Goal Navigation using Goal-Oriented Semantic Exploration. Devendra Singh Chaplot, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov |
| 2020 | Object-Centric Learning with Slot Attention. Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf |
| 2020 | Ode to an ODE. Krzysztof Marcin Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques E. Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani |
| 2020 | Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. Masatoshi Uehara, Masahiro Kato, Shota Yasui |
| 2020 | Off-Policy Evaluation via the Regularized Lagrangian. Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans |
| 2020 | Off-Policy Imitation Learning from Observations. Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou |
| 2020 | Off-Policy Interval Estimation with Lipschitz Value Iteration. Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu |
| 2020 | Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding. Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill |
| 2020 | Offline Imitation Learning with a Misspecified Simulator. Shengyi Jiang, Jing-Cheng Pang, Yang Yu |
| 2020 | On 1/n neural representation and robustness. Josue Nassar, Piotr A. Sokól, SueYeon Chung, Kenneth D. Harris, Il Memming Park |
| 2020 | On Adaptive Attacks to Adversarial Example Defenses. Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry |
| 2020 | On Adaptive Distance Estimation. Yeshwanth Cherapanamjeri, Jelani Nelson |
| 2020 | On Completeness-aware Concept-Based Explanations in Deep Neural Networks. Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar |
| 2020 | On Convergence and Generalization of Dropout Training. Poorya Mianjy, Raman Arora |
| 2020 | On Convergence of Nearest Neighbor Classifiers over Feature Transformations. Luka Rimanic, Cédric Renggli, Bo Li, Ce Zhang |
| 2020 | On Correctness of Automatic Differentiation for Non-Differentiable Functions. Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang |
| 2020 | On Efficiency in Hierarchical Reinforcement Learning. Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh |
| 2020 | On Infinite-Width Hypernetworks. Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang |
| 2020 | On Learning Ising Models under Huber's Contamination Model. Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar |
| 2020 | On Numerosity of Deep Neural Networks. Xi Zhang, Xiaolin Wu |
| 2020 | On Power Laws in Deep Ensembles. Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov |
| 2020 | On Regret with Multiple Best Arms. Yinglun Zhu, Robert Nowak |
| 2020 | On Reward-Free Reinforcement Learning with Linear Function Approximation. Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov |
| 2020 | On Second Order Behaviour in Augmented Neural ODEs. Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió |
| 2020 | On Testing of Samplers. Kuldeep S. Meel, Yash Pote, Sourav Chakraborty |
| 2020 | On Uniform Convergence and Low-Norm Interpolation Learning. Lijia Zhou, Danica J. Sutherland, Nati Srebro |
| 2020 | On Warm-Starting Neural Network Training. Jordan T. Ash, Ryan P. Adams |
| 2020 | On ranking via sorting by estimated expected utility. Clément Calauzènes, Nicolas Usunier |
| 2020 | On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems. Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher |
| 2020 | On the Convergence of Smooth Regularized Approximate Value Iteration Schemes. Elena Smirnova, Elvis Dohmatob |
| 2020 | On the Equivalence between Online and Private Learnability beyond Binary Classification. Young Hun Jung, Baekjin Kim, Ambuj Tewari |
| 2020 | On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu |
| 2020 | On the Error Resistance of Hinge-Loss Minimization. Kunal Talwar |
| 2020 | On the Expressiveness of Approximate Inference in Bayesian Neural Networks. Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner |
| 2020 | On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk |
| 2020 | On the Modularity of Hypernetworks. Tomer Galanti, Lior Wolf |
| 2020 | On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression. Denny Wu, Ji Xu |
| 2020 | On the Power of Louvain in the Stochastic Block Model. Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic |
| 2020 | On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. Ignavier Ng, AmirEmad Ghassami, Kun Zhang |
| 2020 | On the Similarity between the Laplace and Neural Tangent Kernels. Amnon Geifman, Abhay Kumar Yadav, Yoni Kasten, Meirav Galun, David W. Jacobs, Ronen Basri |
| 2020 | On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems. Kaiqing Zhang, Bin Hu, Tamer Basar |
| 2020 | On the Theory of Transfer Learning: The Importance of Task Diversity. Nilesh Tripuraneni, Michael I. Jordan, Chi Jin |
| 2020 | On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples. Richard Y. Zhang |
| 2020 | On the Trade-off between Adversarial and Backdoor Robustness. Cheng-Hsin Weng, Yan-Ting Lee, Shan-Hung Wu |
| 2020 | On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel |
| 2020 | On the distance between two neural networks and the stability of learning. Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu |
| 2020 | On the equivalence of molecular graph convolution and molecular wave function with poor basis set. Masashi Tsubaki, Teruyasu Mizoguchi |
| 2020 | On the linearity of large non-linear models: when and why the tangent kernel is constant. Chaoyue Liu, Libin Zhu, Mikhail Belkin |
| 2020 | On the training dynamics of deep networks with $L_2$ regularization. Aitor Lewkowycz, Guy Gur-Ari |
| 2020 | On the universality of deep learning. Emmanuel Abbe, Colin Sandon |
| 2020 | Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang |
| 2020 | One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers. Heng Yang, Luca Carlone |
| 2020 | One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn |
| 2020 | One-bit Supervision for Image Classification. Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian |
| 2020 | One-sample Guided Object Representation Disassembling. Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song |
| 2020 | Online Adaptation for Consistent Mesh Reconstruction in the Wild. Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz |
| 2020 | Online Agnostic Boosting via Regret Minimization. Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran |
| 2020 | Online Algorithm for Unsupervised Sequential Selection with Contextual Information. Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama |
| 2020 | Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice. Shufan Wang, Jian Li, Shiqiang Wang |
| 2020 | Online Bayesian Goal Inference for Boundedly Rational Planning Agents. Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka |
| 2020 | Online Bayesian Persuasion. Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti |
| 2020 | Online Convex Optimization Over Erdos-Renyi Random Networks. Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi |
| 2020 | Online Decision Based Visual Tracking via Reinforcement Learning. Ke Song, Wei Zhang, Ran Song, Yibin Li |
| 2020 | Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin |
| 2020 | Online Influence Maximization under Linear Threshold Model. Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen |
| 2020 | Online Learning in Contextual Bandits using Gated Linear Networks. Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness |
| 2020 | Online Learning with Primary and Secondary Losses. Avrim Blum, Han Shao |
| 2020 | Online Linear Optimization with Many Hints. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit |
| 2020 | Online MAP Inference of Determinantal Point Processes. Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam |
| 2020 | Online Matrix Completion with Side Information. Mark Herbster, Stephen Pasteris, Lisa Tse |
| 2020 | Online Meta-Critic Learning for Off-Policy Actor-Critic Methods. Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, Timothy M. Hospedales |
| 2020 | Online Multitask Learning with Long-Term Memory. Mark Herbster, Stephen Pasteris, Lisa Tse |
| 2020 | Online Neural Connectivity Estimation with Noisy Group Testing. Anne Draelos, John M. Pearson |
| 2020 | Online Non-Convex Optimization with Imperfect Feedback. Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier |
| 2020 | Online Optimization with Memory and Competitive Control. Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman |
| 2020 | Online Planning with Lookahead Policies. Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor |
| 2020 | Online Robust Regression via SGD on the l1 loss. Scott Pesme, Nicolas Flammarion |
| 2020 | Online Sinkhorn: Optimal Transport distances from sample streams. Arthur Mensch, Gabriel Peyré |
| 2020 | Online Structured Meta-learning. Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, Caiming Xiong |
| 2020 | Online learning with dynamics: A minimax perspective. Kush Bhatia, Karthik Sridharan |
| 2020 | Open Graph Benchmark: Datasets for Machine Learning on Graphs. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec |
| 2020 | Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. John S. Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran |
| 2020 | Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards. Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh |
| 2020 | Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions. Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Emmanouil Zampetakis |
| 2020 | Optimal Best-arm Identification in Linear Bandits. Yassir Jedra, Alexandre Proutière |
| 2020 | Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang |
| 2020 | Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci |
| 2020 | Optimal Learning from Verified Training Data. Nick Bishop, Long Tran-Thanh, Enrico H. Gerding |
| 2020 | Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos |
| 2020 | Optimal Prediction of the Number of Unseen Species with Multiplicity. Yi Hao, Ping Li |
| 2020 | Optimal Private Median Estimation under Minimal Distributional Assumptions. Christos Tzamos, Emmanouil V. Vlatakis-Gkaragkounis, Ilias Zadik |
| 2020 | Optimal Query Complexity of Secure Stochastic Convex Optimization. Wei Tang, Chien-Ju Ho, Yang Liu |
| 2020 | Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms. Alexander Wei, Fred Zhang |
| 2020 | Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds. Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther |
| 2020 | Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization. Dmitry Kovalev, Adil Salim, Peter Richtárik |
| 2020 | Optimal visual search based on a model of target detectability in natural images. Shima Rashidi, Krista A. Ehinger, Andrew Turpin, Lars Kulik |
| 2020 | Optimally Deceiving a Learning Leader in Stackelberg Games. Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco J. Marmolejo Cossío, Ninad Rajgopal, Alexandros A. Voudouris |
| 2020 | Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities. Chaobing Song, Zhengyuan Zhou, Yichao Zhou, Yong Jiang, Yi Ma |
| 2020 | Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks. Kenta Oono, Taiji Suzuki |
| 2020 | Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová |
| 2020 | Optimizing Mode Connectivity via Neuron Alignment. N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai |
| 2020 | Optimizing Neural Networks via Koopman Operator Theory. Akshunna S. Dogra, William T. Redman |
| 2020 | OrganITE: Optimal transplant donor organ offering using an individual treatment effect. Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar |
| 2020 | Organizing recurrent network dynamics by task-computation to enable continual learning. Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo |
| 2020 | Outlier Robust Mean Estimation with Subgaussian Rates via Stability. Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia |
| 2020 | Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora |
| 2020 | Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree. Peizhong Ju, Xiaojun Lin, Jia Liu |
| 2020 | PAC-Bayes Analysis Beyond the Usual Bounds. Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor |
| 2020 | PAC-Bayes Learning Bounds for Sample-Dependent Priors. Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri |
| 2020 | PAC-Bayesian Bound for the Conditional Value at Risk. Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson |
| 2020 | PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun |
| 2020 | PEP: Parameter Ensembling by Perturbation. Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William M. Wells III |
| 2020 | PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks. Minh N. Vu, My T. Thai |
| 2020 | PIE-NET: Parametric Inference of Point Cloud Edges. Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang |
| 2020 | PLANS: Neuro-Symbolic Program Learning from Videos. Raphaël Dang-Nhu |
| 2020 | PLLay: Efficient Topological Layer based on Persistent Landscapes. Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman |
| 2020 | POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis. Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar |
| 2020 | POMDPs in Continuous Time and Discrete Spaces. Bastian Alt, Matthias Schultheis, Heinz Koeppl |
| 2020 | POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min |
| 2020 | PRANK: motion Prediction based on RANKing. Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel |
| 2020 | Parabolic Approximation Line Search for DNNs. Maximus Mutschler, Andreas Zell |
| 2020 | Parameterized Explainer for Graph Neural Network. Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang |
| 2020 | Parametric Instance Classification for Unsupervised Visual Feature learning. Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu |
| 2020 | Part-dependent Label Noise: Towards Instance-dependent Label Noise. Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama |
| 2020 | Partial Optimal Tranport with applications on Positive-Unlabeled Learning. Laetitia Chapel, Mokhtar Z. Alaya, Gilles Gasso |
| 2020 | Partially View-aligned Clustering. Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng |
| 2020 | Passport-aware Normalization for Deep Model Protection. Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu |
| 2020 | Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning. Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis |
| 2020 | Path Integral Based Convolution and Pooling for Graph Neural Networks. Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò |
| 2020 | Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. Alexander Shekhovtsov, Viktor Yanush, Boris Flach |
| 2020 | Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets. Avetik G. Karagulyan, Arnak S. Dalalyan |
| 2020 | Permute-and-Flip: A new mechanism for differentially private selection. Ryan McKenna, Daniel Sheldon |
| 2020 | Personalized Federated Learning with Moreau Envelopes. Canh T. Dinh, Nguyen Hoang Tran, Tuan Dung Nguyen |
| 2020 | Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar |
| 2020 | Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability. Nathan Inkawhich, Kevin J. Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen |
| 2020 | Phase retrieval in high dimensions: Statistical and computational phase transitions. Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová |
| 2020 | Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games. Stephen McAleer, John B. Lanier, Roy Fox, Pierre Baldi |
| 2020 | Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation. Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander G. Hauptmann |
| 2020 | PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals. Henry Charlesworth, Giovanni Montana |
| 2020 | Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko |
| 2020 | Planning with General Objective Functions: Going Beyond Total Rewards. Ruosong Wang, Peilin Zhong, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang |
| 2020 | Point process models for sequence detection in high-dimensional neural spike trains. Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman |
| 2020 | Pointer Graph Networks. Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell |
| 2020 | Policy Improvement via Imitation of Multiple Oracles. Ching-An Cheng, Andrey Kolobov, Alekh Agarwal |
| 2020 | Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond. Gabriele Farina, Tuomas Sandholm |
| 2020 | Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework. Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou |
| 2020 | Position-based Scaled Gradient for Model Quantization and Pruning. Jangho Kim, KiYoon Yoo, Nojun Kwak |
| 2020 | Post-training Iterative Hierarchical Data Augmentation for Deep Networks. Adil Khan, Khadija Fraz |
| 2020 | Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. Bertrand Charpentier, Daniel Zügner, Stephan Günnemann |
| 2020 | Posterior Re-calibration for Imbalanced Datasets. Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira |
| 2020 | Practical Low-Rank Communication Compression in Decentralized Deep Learning. Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi |
| 2020 | Practical No-box Adversarial Attacks against DNNs. Qizhang Li, Yiwen Guo, Hao Chen |
| 2020 | Practical Quasi-Newton Methods for Training Deep Neural Networks. Donald Goldfarb, Yi Ren, Achraf Bahamou |
| 2020 | Pre-training via Paraphrasing. Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer |
| 2020 | Precise expressions for random projections: Low-rank approximation and randomized Newton. Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney |
| 2020 | Predicting Training Time Without Training. Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto |
| 2020 | Prediction with Corrupted Expert Advice. Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni |
| 2020 | Predictive Information Accelerates Learning in RL. Kuang-Huei Lee, Ian Fischer, Anthony Z. Liu, Yijie Guo, Honglak Lee, John F. Canny, Sergio Guadarrama |
| 2020 | Predictive coding in balanced neural networks with noise, chaos and delays. Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli |
| 2020 | Predictive inference is free with the jackknife+-after-bootstrap. Byol Kim, Chen Xu, Rina Foygel Barber |
| 2020 | Preference learning along multiple criteria: A game-theoretic perspective. Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca D. Dragan, Martin J. Wainwright |
| 2020 | Preference-based Reinforcement Learning with Finite-Time Guarantees. Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski |
| 2020 | Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm. Adil Salim, Peter Richtárik |
| 2020 | Primal-Dual Mesh Convolutional Neural Networks. Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone |
| 2020 | Principal Neighbourhood Aggregation for Graph Nets. Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic |
| 2020 | Privacy Amplification via Random Check-Ins. Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta |
| 2020 | Private Identity Testing for High-Dimensional Distributions. Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou |
| 2020 | Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia |
| 2020 | Probabilistic Active Meta-Learning. Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth |
| 2020 | Probabilistic Circuits for Variational Inference in Discrete Graphical Models. Andy Shih, Stefano Ermon |
| 2020 | Probabilistic Fair Clustering. Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson |
| 2020 | Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck |
| 2020 | Probabilistic Linear Solvers for Machine Learning. Jonathan Wenger, Philipp Hennig |
| 2020 | Probabilistic Orientation Estimation with Matrix Fisher Distributions. David Mohlin, Josephine Sullivan, Gérald Bianchi |
| 2020 | Probabilistic Time Series Forecasting with Shape and Temporal Diversity. Vincent Le Guen, Nicolas Thome |
| 2020 | Probably Approximately Correct Constrained Learning. Luiz F. O. Chamon, Alejandro Ribeiro |
| 2020 | Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions. Yi Hao, Alon Orlitsky |
| 2020 | Program Synthesis with Pragmatic Communication. Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama |
| 2020 | Projected Stein Variational Gradient Descent. Peng Chen, Omar Ghattas |
| 2020 | Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh |
| 2020 | Projection Robust Wasserstein Distance and Riemannian Optimization. Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael I. Jordan |
| 2020 | Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. Julien Roy, Paul Barde, Félix G. Harvey, Derek Nowrouzezahrai, Chris Pal |
| 2020 | Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method. Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang |
| 2020 | Prophet Attention: Predicting Attention with Future Attention. Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun |
| 2020 | Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning. Sirisha Rambhatla, Xingguo Li, Jarvis D. Haupt |
| 2020 | Provable Overlapping Community Detection in Weighted Graphs. Jimit Majmudar, Stephen A. Vavasis |
| 2020 | Provably Consistent Partial-Label Learning. Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama |
| 2020 | Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang |
| 2020 | Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach. Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang |
| 2020 | Provably Efficient Neural GTD for Off-Policy Learning. Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong |
| 2020 | Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits. Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts |
| 2020 | Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan |
| 2020 | Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill |
| 2020 | Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill |
| 2020 | Provably Robust Metric Learning. Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh |
| 2020 | Provably adaptive reinforcement learning in metric spaces. Tongyi Cao, Akshay Krishnamurthy |
| 2020 | Proximal Mapping for Deep Regularization. Mao Li, Yingyi Ma, Xinhua Zhang |
| 2020 | Proximity Operator of the Matrix Perspective Function and its Applications. Joong-Ho Won |
| 2020 | Pruning Filter in Filter. Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun |
| 2020 | Pruning neural networks without any data by iteratively conserving synaptic flow. Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli |
| 2020 | Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point. Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov, Anna Vinogradsky, Sarah Massengill, Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka, Xia Song, Subhojit Som, Kaustav Das, Saurabh Tiwary, Steven K. Reinhardt, Sitaram Lanka, Eric S. Chung, Doug Burger |
| 2020 | PyGlove: Symbolic Programming for Automated Machine Learning. Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le |
| 2020 | Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning. Iro Laina, Ruth Fong, Andrea Vedaldi |
| 2020 | Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality. Nian Si, Jose H. Blanchet, Soumyadip Ghosh, Mark S. Squillante |
| 2020 | Quantile Propagation for Wasserstein-Approximate Gaussian Processes. Rui Zhang, Christian J. Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie |
| 2020 | Quantitative Propagation of Chaos for SGD in Wide Neural Networks. Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli |
| 2020 | Quantized Variational Inference. Amir Dib |
| 2020 | R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making. Sergey Shuvaev, Sarah Starosta, Duda Kvitsiani, Ádám Kepecs, Alexei A. Koulakov |
| 2020 | RANet: Region Attention Network for Semantic Segmentation. Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin |
| 2020 | RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning. Riccardo Del Chiaro, Bartlomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer |
| 2020 | RD$^2$: Reward Decomposition with Representation Decomposition. Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu |
| 2020 | RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces. Sébastien Ehrhardt, Oliver Groth, Áron Monszpart, Martin Engelcke, Ingmar Posner, Niloy J. Mitra, Andrea Vedaldi |
| 2020 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas |
| 2020 | RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain |
| 2020 | RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor. Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois C. Knoll |
| 2020 | RandAugment: Practical Automated Data Augmentation with a Reduced Search Space. Ekin Dogus Cubuk, Barret Zoph, Jonathon Shlens, Quoc Le |
| 2020 | Random Reshuffling is Not Always Better. Christopher De Sa |
| 2020 | Random Reshuffling: Simple Analysis with Vast Improvements. Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik |
| 2020 | Random Walk Graph Neural Networks. Giannis Nikolentzos, Michalis Vazirgiannis |
| 2020 | Randomized tests for high-dimensional regression: A more efficient and powerful solution. Yue Li, Ilmun Kim, Yuting Wei |
| 2020 | Rankmax: An Adaptive Projection Alternative to the Softmax Function. Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang |
| 2020 | Ratio Trace Formulation of Wasserstein Discriminant Analysis. Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li |
| 2020 | Rational neural networks. Nicolas Boullé, Yuji Nakatsukasa, Alex Townsend |
| 2020 | Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy |
| 2020 | Real World Games Look Like Spinning Tops. Wojciech M. Czarnecki, Gauthier Gidel, Brendan D. Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi, Max Jaderberg |
| 2020 | Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot |
| 2020 | Reciprocal Adversarial Learning via Characteristic Functions. Shengxi Li, Zeyang Yu, Min Xiang, Danilo P. Mandic |
| 2020 | Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate. Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora |
| 2020 | Reconsidering Generative Objectives For Counterfactual Reasoning. Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin |
| 2020 | Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN. Tao Fang, Yu Qi, Gang Pan |
| 2020 | Recovery of sparse linear classifiers from mixture of responses. Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal |
| 2020 | Recurrent Quantum Neural Networks. Johannes Bausch |
| 2020 | Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman |
| 2020 | Recursive Inference for Variational Autoencoders. Minyoung Kim, Vladimir Pavlovic |
| 2020 | Reducing Adversarially Robust Learning to Non-Robust PAC Learning. Omar Montasser, Steve Hanneke, Nati Srebro |
| 2020 | Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals. Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su |
| 2020 | Regression with reject option and application to kNN. Ahmed Zaoui, Christophe Denis, Mohamed Hebiri |
| 2020 | Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses. Yihan Zhou, Victor S. Portella, Mark Schmidt, Nicholas J. A. Harvey |
| 2020 | Regret in Online Recommendation Systems. Kaito Ariu, Narae Ryu, Se-Young Yun, Alexandre Proutière |
| 2020 | Regularized linear autoencoders recover the principal components, eventually. Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse |
| 2020 | Regularizing Black-box Models for Improved Interpretability. Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar |
| 2020 | Regularizing Towards Permutation Invariance In Recurrent Models. Edo Cohen-Karlik, Avichai Ben David, Amir Globerson |
| 2020 | Reinforced Molecular Optimization with Neighborhood-Controlled Grammars. Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang |
| 2020 | Reinforcement Learning for Control with Multiple Frequencies. Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim |
| 2020 | Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting. Ziping Xu, Ambuj Tewari |
| 2020 | Reinforcement Learning with Augmented Data. Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas |
| 2020 | Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. Arthur Delarue, Ross Anderson, Christian Tjandraatmadja |
| 2020 | Reinforcement Learning with Feedback Graphs. Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan |
| 2020 | Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension. Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang |
| 2020 | Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D. Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng |
| 2020 | RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder. Cheng Chi, Fangyun Wei, Han Hu |
| 2020 | Relative gradient optimization of the Jacobian term in unsupervised deep learning. Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen |
| 2020 | Reliable Graph Neural Networks via Robust Aggregation. Simon Geisler, Daniel Zügner, Stephan Günnemann |
| 2020 | Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies. Itai Gat, Idan Schwartz, Alexander G. Schwing, Tamir Hazan |
| 2020 | RepPoints v2: Verification Meets Regression for Object Detection. Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu |
| 2020 | Reparameterizing Mirror Descent as Gradient Descent. Ehsan Amid, Manfred K. Warmuth |
| 2020 | Replica-Exchange Nosé-Hoover Dynamics for Bayesian Learning on Large Datasets. Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang |
| 2020 | Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment. Yuan Chen, Donglin Zeng, Tianchen Xu, Yuanjia Wang |
| 2020 | Rescuing neural spike train models from bad MLE. Diego M. Arribas, Yuan Zhao, Il Memming Park |
| 2020 | Reservoir Computing meets Recurrent Kernels and Structured Transforms. Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan, Florent Krzakala |
| 2020 | Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts. Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei Zhang, Jiashi Feng, Tong Zhang |
| 2020 | Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis. Ye Yuan, Kris Kitani |
| 2020 | Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits. Siwei Wang, Longbo Huang, John C. S. Lui |
| 2020 | Restoring Negative Information in Few-Shot Object Detection. Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li |
| 2020 | Rethinking Importance Weighting for Deep Learning under Distribution Shift. Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama |
| 2020 | Rethinking Learnable Tree Filter for Generic Feature Transform. Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Xiangyu Zhang, Hongbin Sun, Jian Sun, Nanning Zheng |
| 2020 | Rethinking Pre-training and Self-training. Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le |
| 2020 | Rethinking pooling in graph neural networks. Diego Mesquita, Amauri H. Souza Jr., Samuel Kaski |
| 2020 | Rethinking the Value of Labels for Improving Class-Imbalanced Learning. Yuzhe Yang, Zhi Xu |
| 2020 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela |
| 2020 | RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist. Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, Junzhou Huang |
| 2020 | Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice. Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, International Brain Laboratory, Ila Fiete |
| 2020 | Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity. Dan Garber |
| 2020 | Revisiting Parameter Sharing for Automatic Neural Channel Number Search. Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang, Irwin King, Michael R. Lyu, Jian Cheng |
| 2020 | Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. Quang Minh Hoang, Nghia Hoang, Hai Pham, David P. Woodruff |
| 2020 | Reward Propagation Using Graph Convolutional Networks. Martin Klissarov, Doina Precup |
| 2020 | Reward-rational (implicit) choice: A unifying formalism for reward learning. Hong Jun Jeon, Smitha Milli, Anca D. Dragan |
| 2020 | Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement. Ben Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov |
| 2020 | Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster |
| 2020 | Riemannian Continuous Normalizing Flows. Emile Mathieu, Maximilian Nickel |
| 2020 | Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret. Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie |
| 2020 | Robust Correction of Sampling Bias using Cumulative Distribution Functions. Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck |
| 2020 | Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations. Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane S. Boning, Cho-Jui Hsieh |
| 2020 | Robust Density Estimation under Besov IPM Losses. Ananya Uppal, Shashank Singh, Barnabás Póczos |
| 2020 | Robust Disentanglement of a Few Factors at a Time. Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik |
| 2020 | Robust Federated Learning: The Case of Affine Distribution Shifts. Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie |
| 2020 | Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. Jerry Li, Guanghao Ye |
| 2020 | Robust Meta-learning for Mixed Linear Regression with Small Batches. Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh |
| 2020 | Robust Multi-Agent Reinforcement Learning with Model Uncertainty. Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar |
| 2020 | Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian. Yunpeng Shi, Shaohan Li, Gilad Lerman |
| 2020 | Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. Yogesh Balaji, Rama Chellappa, Soheil Feizi |
| 2020 | Robust Optimization for Fairness with Noisy Protected Groups. Serena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan |
| 2020 | Robust Persistence Diagrams using Reproducing Kernels. Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur |
| 2020 | Robust Pre-Training by Adversarial Contrastive Learning. Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang |
| 2020 | Robust Quantization: One Model to Rule Them All. Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex M. Bronstein, Uri C. Weiser |
| 2020 | Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization. Chong You, Zhihui Zhu, Qing Qu, Yi Ma |
| 2020 | Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar |
| 2020 | Robust Reinforcement Learning via Adversarial training with Langevin Dynamics. Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, Volkan Cevher |
| 2020 | Robust Sequence Submodular Maximization. Gamal Sallam, Zizhan Zheng, Jie Wu, Bo Ji |
| 2020 | Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. Arun Jambulapati, Jerry Li, Kevin Tian |
| 2020 | Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. Samuel B. Hopkins, Jerry Li, Fred Zhang |
| 2020 | Robust compressed sensing using generative models. Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis |
| 2020 | Robust large-margin learning in hyperbolic space. Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar |
| 2020 | Robust, Accurate Stochastic Optimization for Variational Inference. Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari |
| 2020 | Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov |
| 2020 | Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. Kevin Scaman, Cédric Malherbe |
| 2020 | Robustness of Bayesian Neural Networks to Gradient-Based Attacks. Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patané, Luca Bortolussi, Guido Sanguinetti |
| 2020 | Robustness of Community Detection to Random Geometric Perturbations. Sandrine Péché, Vianney Perchet |
| 2020 | Rotated Binary Neural Network. Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin |
| 2020 | Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud. Seohyun Kim, Jaeyoo Park, Bohyung Han |
| 2020 | SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection. Xiaoya Li, Yuxian Meng, Mingxin Zhou, Qinghong Han, Fei Wu, Jiwei Li |
| 2020 | SCOP: Scientific Control for Reliable Neural Network Pruning. Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, Chang Xu |
| 2020 | SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images. Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey |
| 2020 | SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling |
| 2020 | SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology. Mark S. Veillette, Siddharth Samsi, Christopher J. Mattioli |
| 2020 | SGD with shuffling: optimal rates without component convexity and large epoch requirements. Kwangjun Ahn, Chulhee Yun, Suvrit Sra |
| 2020 | SIRI: Spatial Relation Induced Network For Spatial Description Resolution. Peiyao Wang, Weixin Luo, Yanyu Xu, Haojie Li, Shugong Xu, Jianyu Yang, Shenghua Gao |
| 2020 | SLIP: Learning to predict in unknown dynamical systems with long-term memory. Paria Rashidinejad, Jiantao Jiao, Stuart Russell |
| 2020 | SMYRF - Efficient Attention using Asymmetric Clustering. Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis |
| 2020 | SOLOv2: Dynamic and Fast Instance Segmentation. Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen |
| 2020 | STEER : Simple Temporal Regularization For Neural ODE. Arnab Ghosh, Harkirat S. Behl, Emilien Dupont, Philip H. S. Torr, Vinay P. Namboodiri |
| 2020 | STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks. Meiyi Ma, Ji Gao, Lu Feng, John A. Stankovic |
| 2020 | SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm. Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar |
| 2020 | SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet |
| 2020 | Safe Reinforcement Learning via Curriculum Induction. Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal |
| 2020 | Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction. Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen |
| 2020 | Sample Complexity of Uniform Convergence for Multicalibration. Eliran Shabat, Lee Cohen, Yishay Mansour |
| 2020 | Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang |
| 2020 | Sample complexity and effective dimension for regression on manifolds. Andrew D. McRae, Justin Romberg, Mark A. Davenport |
| 2020 | Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato |
| 2020 | Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu |
| 2020 | Sampling from a k-DPP without looking at all items. Daniele Calandriello, Michal Derezinski, Michal Valko |
| 2020 | Sampling-Decomposable Generative Adversarial Recommender. Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen |
| 2020 | Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot. Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee |
| 2020 | Scalable Belief Propagation via Relaxed Scheduling. Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen |
| 2020 | Scalable Graph Neural Networks via Bidirectional Propagation. Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen |
| 2020 | Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward. Guannan Qu, Yiheng Lin, Adam Wierman, Na Li |
| 2020 | ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan |
| 2020 | Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. Yimeng Min, Frederik Wenkel, Guy Wolf |
| 2020 | Searching for Low-Bit Weights in Quantized Neural Networks. Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing Xu, Chao Xu, Dacheng Tao, Chang Xu |
| 2020 | Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking. Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari |
| 2020 | Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin |
| 2020 | Secretary and Online Matching Problems with Machine Learned Advice. Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, Pavel Kolev |
| 2020 | Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms. Pinar Ozisik, Philip S. Thomas |
| 2020 | See, Hear, Explore: Curiosity via Audio-Visual Association. Victoria Dean, Shubham Tulsiani, Abhinav Gupta |
| 2020 | Self-Adaptive Training: beyond Empirical Risk Minimization. Lang Huang, Chao Zhang, Hongyang Zhang |
| 2020 | Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs. Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory G. Slabaugh, Qi Tian |
| 2020 | Self-Distillation Amplifies Regularization in Hilbert Space. Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett |
| 2020 | Self-Distillation as Instance-Specific Label Smoothing. Zhilu Zhang, Mert R. Sabuncu |
| 2020 | Self-Imitation Learning via Generalized Lower Bound Q-learning. Yunhao Tang |
| 2020 | Self-Learning Transformations for Improving Gaze and Head Redirection. Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges |
| 2020 | Self-Paced Deep Reinforcement Learning. Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen |
| 2020 | Self-Supervised Few-Shot Learning on Point Clouds. Charu Sharma, Manohar Kaul |
| 2020 | Self-Supervised Generative Adversarial Compression. Chong Yu, Jeff Pool |
| 2020 | Self-Supervised Graph Transformer on Large-Scale Molecular Data. Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang |
| 2020 | Self-Supervised Learning by Cross-Modal Audio-Video Clustering. Humam Alwassel, Dhruv Mahajan, Bruno Korbar, Lorenzo Torresani, Bernard Ghanem, Du Tran |
| 2020 | Self-Supervised MultiModal Versatile Networks. Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelovic, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman |
| 2020 | Self-Supervised Relational Reasoning for Representation Learning. Massimiliano Patacchiola, Amos J. Storkey |
| 2020 | Self-Supervised Relationship Probing. Jiuxiang Gu, Jason Kuen, Shafiq R. Joty, Jianfei Cai, Vlad I. Morariu, Handong Zhao, Tong Sun |
| 2020 | Self-Supervised Visual Representation Learning from Hierarchical Grouping. Xiao Zhang, Michael Maire |
| 2020 | Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID. Yixiao Ge, Feng Zhu, Dapeng Chen, Rui Zhao, Hongsheng Li |
| 2020 | Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs. Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim |
| 2020 | Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman |
| 2020 | Self-supervised learning through the eyes of a child. A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake |
| 2020 | Self-training Avoids Using Spurious Features Under Domain Shift. Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma |
| 2020 | Semantic Visual Navigation by Watching YouTube Videos. Matthew Chang, Arjun Gupta, Saurabh Gupta |
| 2020 | Semi-Supervised Neural Architecture Search. Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu |
| 2020 | Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. Wei Wang, Min-Ling Zhang |
| 2020 | Semialgebraic Optimization for Lipschitz Constants of ReLU Networks. Tong Chen, Jean B. Lasserre, Victor Magron, Edouard Pauwels |
| 2020 | Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding. Victor Veitch, Anisha Zaveri |
| 2020 | Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals. Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie |
| 2020 | Sequential Bayesian Experimental Design with Variable Cost Structure. Sue Zheng, David S. Hayden, Jason Pacheco, John W. Fisher III |
| 2020 | Set2Graph: Learning Graphs From Sets. Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman |
| 2020 | ShapeFlow: Learnable Deformation Flows Among 3D Shapes. Chiyu Max Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas |
| 2020 | Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning. Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht |
| 2020 | Shared Space Transfer Learning for analyzing multi-site fMRI data. Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner |
| 2020 | Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth. Guy Bresler, Dheeraj Nagaraj |
| 2020 | Sharp uniform convergence bounds through empirical centralization. Cyrus Cousins, Matteo Riondato |
| 2020 | Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite |
| 2020 | Sharper Generalization Bounds for Pairwise Learning. Yunwen Lei, Antoine Ledent, Marius Kloft |
| 2020 | ShiftAddNet: A Hardware-Inspired Deep Network. Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin |
| 2020 | Simple and Fast Algorithm for Binary Integer and Online Linear Programming. Xiaocheng Li, Chunlin Sun, Yinyu Ye |
| 2020 | Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan |
| 2020 | Simple and Scalable Sparse k-means Clustering via Feature Ranking. Zhiyue Zhang, Kenneth Lange, Jason Xu |
| 2020 | Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin |
| 2020 | Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson |
| 2020 | Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David D. Cox, James J. DiCarlo |
| 2020 | Simultaneous Preference and Metric Learning from Paired Comparisons. Austin Xu, Mark A. Davenport |
| 2020 | Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition. Tiancheng Jin, Haipeng Luo |
| 2020 | Sinkhorn Barycenter via Functional Gradient Descent. Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani |
| 2020 | Sinkhorn Natural Gradient for Generative Models. Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani |
| 2020 | Skeleton-bridged Point Completion: From Global Inference to Local Adjustment. Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian J. Zhang |
| 2020 | Sliding Window Algorithms for k-Clustering Problems. Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam |
| 2020 | Small Nash Equilibrium Certificates in Very Large Games. Brian Hu Zhang, Tuomas Sandholm |
| 2020 | Smooth And Consistent Probabilistic Regression Trees. Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim |
| 2020 | Smoothed Analysis of Online and Differentially Private Learning. Nika Haghtalab, Tim Roughgarden, Abhishek Shetty |
| 2020 | Smoothed Geometry for Robust Attribution. Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, Anupam Datta |
| 2020 | Smoothly Bounding User Contributions in Differential Privacy. Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab S. Mirrokni, Lijie Ren |
| 2020 | SnapBoost: A Heterogeneous Boosting Machine. Thomas P. Parnell, Andreea Anghel, Malgorzata Lazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis |
| 2020 | Soft Contrastive Learning for Visual Localization. Janine Thoma, Danda Pani Paudel, Luc Van Gool |
| 2020 | SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds. Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim |
| 2020 | Softmax Deep Double Deterministic Policy Gradients. Ling Pan, Qingpeng Cai, Longbo Huang |
| 2020 | Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey |
| 2020 | Space-Time Correspondence as a Contrastive Random Walk. Allan Jabri, Andrew Owens, Alexei A. Efros |
| 2020 | Sparse Graphical Memory for Robust Planning. Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak |
| 2020 | Sparse Learning with CART. Jason M. Klusowski |
| 2020 | Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning. Anthony Tompkins, Rafael Oliveira, Fabio T. Ramos |
| 2020 | Sparse Symplectically Integrated Neural Networks. Daniel M. DiPietro, Shiying Xiong, Bo Zhu |
| 2020 | Sparse Weight Activation Training. Md Aamir Raihan, Tor M. Aamodt |
| 2020 | Sparse and Continuous Attention Mechanisms. André F. T. Martins, António Farinhas, Marcos V. Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo |
| 2020 | Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks. Zhou Fan, Zhichao Wang |
| 2020 | Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang |
| 2020 | Spike and slab variational Bayes for high dimensional logistic regression. Kolyan Ray, Botond Szabó, Gabriel Clara |
| 2020 | Spin-Weighted Spherical CNNs. Carlos Esteves, Ameesh Makadia, Kostas Daniilidis |
| 2020 | Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar |
| 2020 | Stable and expressive recurrent vision models. Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex G. Liu, Thomas Serre |
| 2020 | Stage-wise Conservative Linear Bandits. Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh |
| 2020 | Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes. Yuval Emek, Ron Lavi, Rad Niazadeh, Yangguang Shi |
| 2020 | Stationary Activations for Uncertainty Calibration in Deep Learning. Lassi Meronen, Christabella Irwanto, Arno Solin |
| 2020 | Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits. Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet |
| 2020 | Statistical Guarantees of Distributed Nearest Neighbor Classification. Jiexin Duan, Xingye Qiao, Guang Cheng |
| 2020 | Statistical Optimal Transport posed as Learning Kernel Embedding. Jagarlapudi Saketha Nath, Pratik Kumar Jawanpuria |
| 2020 | Statistical and Topological Properties of Sliced Probability Divergences. Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli |
| 2020 | Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. Jérôme-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion |
| 2020 | Statistical-Query Lower Bounds via Functional Gradients. Surbhi Goel, Aravind Gollakota, Adam R. Klivans |
| 2020 | Steady State Analysis of Episodic Reinforcement Learning. Bojun Huang |
| 2020 | Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction. Benoît Colange, Jaakko Peltonen, Michaël Aupetit, Denys Dutykh, Sylvain Lespinats |
| 2020 | Stein Self-Repulsive Dynamics: Benefits From Past Samples. Mao Ye, Tongzheng Ren, Qiang Liu |
| 2020 | Stochastic Deep Gaussian Processes over Graphs. Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia |
| 2020 | Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes. Hao Chen, Lili Zheng, Raed Al Kontar, Garvesh Raskutti |
| 2020 | Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine |
| 2020 | Stochastic Normalization. Zhi Kou, Kaichao You, Mingsheng Long, Jianmin Wang |
| 2020 | Stochastic Normalizing Flows. Hao Wu, Jonas Köhler, Frank Noé |
| 2020 | Stochastic Optimization for Performative Prediction. Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic, Moritz Hardt |
| 2020 | Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. Eduard Gorbunov, Marina Danilova, Alexander V. Gasnikov |
| 2020 | Stochastic Optimization with Laggard Data Pipelines. Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang |
| 2020 | Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang |
| 2020 | Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker |
| 2020 | Stochastic Stein Discrepancies. Jackson Gorham, Anant Raj, Lester Mackey |
| 2020 | Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. Lingkai Kong, Molei Tao |
| 2020 | Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. Jianda Chen, Shangyu Chen, Sinno Jialin Pan |
| 2020 | StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks. Guangmo Tong |
| 2020 | Strictly Batch Imitation Learning by Energy-based Distribution Matching. Daniel Jarrett, Ioana Bica, Mihaela van der Schaar |
| 2020 | Strongly Incremental Constituency Parsing with Graph Neural Networks. Kaiyu Yang, Jia Deng |
| 2020 | Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering. Meng Liu, David F. Gleich |
| 2020 | Structured Convolutions for Efficient Neural Network Design. Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli |
| 2020 | Structured Prediction for Conditional Meta-Learning. Ruohan Wang, Yiannis Demiris, Carlo Ciliberto |
| 2020 | Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces. Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh |
| 2020 | Sub-sampling for Efficient Non-Parametric Bandit Exploration. Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard |
| 2020 | Subgraph Neural Networks. Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik |
| 2020 | Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. Yueming Lyu, Yuan Yuan, Ivor W. Tsang |
| 2020 | Submodular Maximization Through Barrier Functions. Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrák |
| 2020 | Submodular Meta-Learning. Arman Adibi, Aryan Mokhtari, Hamed Hassani |
| 2020 | Succinct and Robust Multi-Agent Communication With Temporal Message Control. Sai Qian Zhang, Qi Zhang, Jieyu Lin |
| 2020 | Sufficient dimension reduction for classification using principal optimal transport direction. Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong |
| 2020 | SuperLoss: A Generic Loss for Robust Curriculum Learning. Thibault Castells, Philippe Weinzaepfel, Jérôme Revaud |
| 2020 | Supermasks in Superposition. Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi |
| 2020 | Supervised Contrastive Learning. Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan |
| 2020 | SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling |
| 2020 | Swapping Autoencoder for Deep Image Manipulation. Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang |
| 2020 | Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez |
| 2020 | Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis. Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song |
| 2020 | Synthesizing Tasks for Block-based Programming. Umair Z. Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish Singla |
| 2020 | Synthetic Data Generators - Sequential and Private. Olivier Bousquet, Roi Livni, Shay Moran |
| 2020 | System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina. Cornelius Schröder, David A. Klindt, Sarah Strauß, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens |
| 2020 | TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation. Dongxu Li, Chenchen Xu, Xin Yu, Kaihao Zhang, Benjamin Swift, Hanna Suominen, Hongdong Li |
| 2020 | Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor |
| 2020 | Taming Discrete Integration via the Boon of Dimensionality. Jeffrey M. Dudek, Dror Fried, Kuldeep S. Meel |
| 2020 | Targeted Adversarial Perturbations for Monocular Depth Prediction. Alex Wong, Safa Cicek, Stefano Soatto |
| 2020 | Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams |
| 2020 | Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes. Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao |
| 2020 | Task-Oriented Feature Distillation. Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao |
| 2020 | Task-Robust Model-Agnostic Meta-Learning. Liam Collins, Aryan Mokhtari, Sanjay Shakkottai |
| 2020 | Task-agnostic Exploration in Reinforcement Learning. Xuezhou Zhang, Yuzhe Ma, Adish Singla |
| 2020 | TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation. Chun-Hsing Lin, Siang-Ruei Wu, Hung-yi Lee, Yun-Nung Chen |
| 2020 | Teaching a GAN What Not to Learn. Siddarth Asokan, Chandra Sekhar Seelamantula |
| 2020 | Telescoping Density-Ratio Estimation. Benjamin Rhodes, Kai Xu, Michael U. Gutmann |
| 2020 | Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang |
| 2020 | Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks. Wenrui Zhang, Peng Li |
| 2020 | Temporal Variability in Implicit Online Learning. Nicolò Campolongo, Francesco Orabona |
| 2020 | Tensor Completion Made Practical. Allen Liu, Ankur Moitra |
| 2020 | Testing Determinantal Point Processes. Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka |
| 2020 | Texture Interpolation for Probing Visual Perception. Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen Cagli |
| 2020 | The Adaptive Complexity of Maximizing a Gross Substitutes Valuation. Ron Kupfer, Sharon Qian, Eric Balkanski, Yaron Singer |
| 2020 | The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto |
| 2020 | The All-or-Nothing Phenomenon in Sparse Tensor PCA. Jonathan Niles-Weed, Ilias Zadik |
| 2020 | The Autoencoding Variational Autoencoder. A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli |
| 2020 | The Complete Lasso Tradeoff Diagram. Hua Wang, Yachong Yang, Zhiqi Bu, Weijie J. Su |
| 2020 | The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi |
| 2020 | The Cone of Silence: Speech Separation by Localization. Teerapat Jenrungrot, Vivek Jayaram, Steven M. Seitz, Ira Kemelmacher-Shlizerman |
| 2020 | The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification. Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, Krunal Patel, Juan Pablo Vielma |
| 2020 | The Convolution Exponential and Generalized Sylvester Flows. Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling |
| 2020 | The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. Yingxiang Yang, Negar Kiyavash, Le Song, Niao He |
| 2020 | The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification. Yihao Lv, Youzhi Gu, Xinggao Liu |
| 2020 | The Discrete Gaussian for Differential Privacy. Clément L. Canonne, Gautam Kamath, Thomas Steinke |
| 2020 | The Diversified Ensemble Neural Network. Shaofeng Zhang, Meng Liu, Junchi Yan |
| 2020 | The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. Adam D. Smith, Shuang Song, Abhradeep Thakurta |
| 2020 | The Generalization-Stability Tradeoff In Neural Network Pruning. Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu, Gordon Erlebacher |
| 2020 | The Generalized Lasso with Nonlinear Observations and Generative Priors. Zhaoqiang Liu, Jonathan Scarlett |
| 2020 | The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine |
| 2020 | The Implications of Local Correlation on Learning Some Deep Functions. Eran Malach, Shai Shalev-Shwartz |
| 2020 | The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar |
| 2020 | The Lottery Ticket Hypothesis for Pre-trained BERT Networks. Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin |
| 2020 | The MAGICAL Benchmark for Robust Imitation. Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell |
| 2020 | The Mean-Squared Error of Double Q-Learning. Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant |
| 2020 | The NetHack Learning Environment. Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel |
| 2020 | The Origins and Prevalence of Texture Bias in Convolutional Neural Networks. Katherine L. Hermann, Ting Chen, Simon Kornblith |
| 2020 | The Pitfalls of Simplicity Bias in Neural Networks. Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli |
| 2020 | The Potts-Ising model for discrete multivariate data. Zahra S. Razaee, Arash A. Amini |
| 2020 | The Power of Comparisons for Actively Learning Linear Classifiers. Max Hopkins, Daniel Kane, Shachar Lovett |
| 2020 | The Power of Predictions in Online Control. Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman |
| 2020 | The Primal-Dual method for Learning Augmented Algorithms. Étienne Bamas, Andreas Maggiori, Ola Svensson |
| 2020 | The Smoothed Possibility of Social Choice. Lirong Xia |
| 2020 | The Statistical Complexity of Early-Stopped Mirror Descent. Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini |
| 2020 | The Statistical Cost of Robust Kernel Hyperparameter Turning. Raphael A. Meyer, Christopher Musco |
| 2020 | The Strong Screening Rule for SLOPE. Johan Larsson, Malgorzata Bogdan, Jonas Wallin |
| 2020 | The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks. Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington |
| 2020 | The Value Equivalence Principle for Model-Based Reinforcement Learning. Christopher Grimm, André Barreto, Satinder Singh, David Silver |
| 2020 | The Wasserstein Proximal Gradient Algorithm. Adil Salim, Anna Korba, Giulia Luise |
| 2020 | The interplay between randomness and structure during learning in RNNs. Friedrich Schüßler, Francesca Mastrogiuseppe, Alexis M. Dubreuil, Srdjan Ostojic, Omri Barak |
| 2020 | The phase diagram of approximation rates for deep neural networks. Dmitry Yarotsky, Anton Zhevnerchuk |
| 2020 | The route to chaos in routing games: When is price of anarchy too optimistic? Thiparat Chotibut, Fryderyk Falniowski, Michal Misiurewicz, Georgios Piliouras |
| 2020 | Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi |
| 2020 | Theory-Inspired Path-Regularized Differential Network Architecture Search. Pan Zhou, Caiming Xiong, Richard Socher, Steven Chu-Hong Hoi |
| 2020 | Throughput-Optimal Topology Design for Cross-Silo Federated Learning. Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal |
| 2020 | Thunder: a Fast Coordinate Selection Solver for Sparse Learning. Shaogang Ren, Weijie Zhao, Ping Li |
| 2020 | Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits. Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida |
| 2020 | Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. Raphaël Berthier, Francis R. Bach, Pierre Gaillard |
| 2020 | Tight last-iterate convergence rates for no-regret learning in multi-player games. Noah Golowich, Sarath Pattathil, Constantinos Daskalakis |
| 2020 | Time-Reversal Symmetric ODE Network. In Huh, Eunho Yang, Sung Ju Hwang, Jinwoo Shin |
| 2020 | Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. Lifeng Shen, Zhuocong Li, James T. Kwok |
| 2020 | TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning. Han Cai, Chuang Gan, Ligeng Zhu, Song Han |
| 2020 | Top-KAST: Top-K Always Sparse Training. Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, Erich Elsen |
| 2020 | Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples. Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena |
| 2020 | TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search. Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul M. Zimmerman |
| 2020 | Toward the Fundamental Limits of Imitation Learning. Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran |
| 2020 | Towards Better Generalization of Adaptive Gradient Methods. Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li |
| 2020 | Towards Convergence Rate Analysis of Random Forests for Classification. Wei Gao, Zhi-Hua Zhou |
| 2020 | Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts. Max Ryabinin, Anton Gusev |
| 2020 | Towards Deeper Graph Neural Networks with Differentiable Group Normalization. Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu |
| 2020 | Towards Interpretable Natural Language Understanding with Explanations as Latent Variables. Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang |
| 2020 | Towards Learning Convolutions from Scratch. Behnam Neyshabur |
| 2020 | Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples. Jay Nandy, Wynne Hsu, Mong-Li Lee |
| 2020 | Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes. Yi Tian, Jian Qian, Suvrit Sra |
| 2020 | Towards More Practical Adversarial Attacks on Graph Neural Networks. Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei |
| 2020 | Towards Neural Programming Interfaces. Zachary Brown, Nathaniel R. Robinson, David Wingate, Nancy Fulda |
| 2020 | Towards Playing Full MOBA Games with Deep Reinforcement Learning. Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu |
| 2020 | Towards Problem-dependent Optimal Learning Rates. Yunbei Xu, Assaf Zeevi |
| 2020 | Towards Safe Policy Improvement for Non-Stationary MDPs. Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas |
| 2020 | Towards Scalable Bayesian Learning of Causal DAGs. Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto |
| 2020 | Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs. Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su |
| 2020 | Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning. Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu-Hong Hoi, Weinan E |
| 2020 | Towards Understanding Hierarchical Learning: Benefits of Neural Representations. Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher |
| 2020 | Towards a Better Global Loss Landscape of GANs. Ruoyu Sun, Tiantian Fang, Alexander G. Schwing |
| 2020 | Towards a Combinatorial Characterization of Bounded-Memory Learning. Alon Gonen, Shachar Lovett, Michal Moshkovitz |
| 2020 | Towards practical differentially private causal graph discovery. Lun Wang, Qi Pang, Dawn Song |
| 2020 | Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation. Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon |
| 2020 | Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering. Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong |
| 2020 | Train-by-Reconnect: Decoupling Locations of Weights from Their Values. Yushi Qiu, Reiji Suda |
| 2020 | Training Generative Adversarial Networks by Solving Ordinary Differential Equations. Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli |
| 2020 | Training Generative Adversarial Networks with Limited Data. Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila |
| 2020 | Training Linear Finite-State Machines. Arash Ardakani, Amir Ardakani, Warren J. Gross |
| 2020 | Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification. Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe |
| 2020 | Training Stronger Baselines for Learning to Optimize. Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang |
| 2020 | Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel |
| 2020 | Transfer Learning via ℓ Masaaki Takada, Hironori Fujisawa |
| 2020 | Transferable Calibration with Lower Bias and Variance in Domain Adaptation. Ximei Wang, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2020 | Transferable Graph Optimizers for ML Compilers. Yanqi Zhou, Sudip Roy, AmirAli Abdolrashidi, Daniel Wong, Peter C. Ma, Qiumin Xu, Hanxiao Liu, Mangpo Phitchaya Phothilimtha, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon |
| 2020 | Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding. Rishi Sonthalia, Anna C. Gilbert |
| 2020 | Triple descent and the two kinds of overfitting: where & why do they appear? Stéphane d'Ascoli, Levent Sagun, Giulio Biroli |
| 2020 | Truncated Linear Regression in High Dimensions. Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis |
| 2020 | Trust the Model When It Is Confident: Masked Model-based Actor-Critic. Feiyang Pan, Jia He, Dandan Tu, Qing He |
| 2020 | Truthful Data Acquisition via Peer Prediction. Yiling Chen, Yiheng Shen, Shuran Zheng |
| 2020 | UCLID-Net: Single View Reconstruction in Object Space. Benoît Guillard, Edoardo Remelli, Pascal Fua |
| 2020 | UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree. Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz |
| 2020 | UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging. Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, In So Kweon |
| 2020 | UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection. Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang |
| 2020 | Ultra-Low Precision 4-bit Training of Deep Neural Networks. Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan |
| 2020 | Ultrahyperbolic Representation Learning. Marc T. Law, Jos Stam |
| 2020 | UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging. Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi |
| 2020 | Unbalanced Sobolev Descent. Youssef Mroueh, Mattia Rigotti |
| 2020 | Uncertainty Aware Semi-Supervised Learning on Graph Data. Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho |
| 2020 | Uncertainty Quantification for Inferring Hawkes Networks. Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie |
| 2020 | Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation. Ping Hu, Stan Sclaroff, Kate Saenko |
| 2020 | Uncertainty-aware Self-training for Few-shot Text Classification. Subhabrata Mukherjee, Ahmed Hassan Awadallah |
| 2020 | Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy |
| 2020 | Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features. Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang |
| 2020 | Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks. Ryo Karakida, Kazuki Osawa |
| 2020 | Understanding Deep Architecture with Reasoning Layer. Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song |
| 2020 | Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition. Ben Adlam, Jeffrey Pennington |
| 2020 | Understanding Global Feature Contributions With Additive Importance Measures. Ian Covert, Scott M. Lundberg, Su-In Lee |
| 2020 | Understanding Gradient Clipping in Private SGD: A Geometric Perspective. Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong |
| 2020 | Understanding and Exploring the Network with Stochastic Architectures. Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu |
| 2020 | Understanding and Improving Fast Adversarial Training. Maksym Andriushchenko, Nicolas Flammarion |
| 2020 | Understanding spiking networks through convex optimization. Allan Mancoo, Sander W. Keemink, Christian K. Machens |
| 2020 | Understanding the Role of Training Regimes in Continual Learning. Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh |
| 2020 | Unfolding recurrence by Green's functions for optimized reservoir computing. Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias |
| 2020 | Unfolding the Alternating Optimization for Blind Super Resolution. Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan |
| 2020 | Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks. Jinseok Kim, Kyungsu Kim, Jae-Joon Kim |
| 2020 | Universal Domain Adaptation through Self Supervision. Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko |
| 2020 | Universal Function Approximation on Graphs. Rickard Brüel Gabrielsson |
| 2020 | Universal guarantees for decision tree induction via a higher-order splitting criterion. Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan |
| 2020 | Universally Quantized Neural Compression. Eirikur Agustsson, Lucas Theis |
| 2020 | Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms. Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi |
| 2020 | Unsupervised Data Augmentation for Consistency Training. Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, Quoc Le |
| 2020 | Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models. Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro |
| 2020 | Unsupervised Learning of Dense Visual Representations. Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron C. Courville |
| 2020 | Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control. Yaofeng Desmond Zhong, Naomi Ehrich Leonard |
| 2020 | Unsupervised Learning of Object Landmarks via Self-Training Correspondence. Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos |
| 2020 | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin |
| 2020 | Unsupervised Representation Learning by Invariance Propagation. Feng Wang, Huaping Liu, Di Guo, Fuchun Sun |
| 2020 | Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning. Tao Han, Junyu Gao, Yuan Yuan, Qi Wang |
| 2020 | Unsupervised Sound Separation Using Mixture Invariant Training. Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron J. Weiss, Kevin W. Wilson, John R. Hershey |
| 2020 | Unsupervised Text Generation by Learning from Search. Jingjing Li, Zichao Li, Lili Mou, Xin Jiang, Michael R. Lyu, Irwin King |
| 2020 | Unsupervised Translation of Programming Languages. Baptiste Rozière, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample |
| 2020 | Unsupervised object-centric video generation and decomposition in 3D. Paul Henderson, Christoph H. Lampert |
| 2020 | Untangling tradeoffs between recurrence and self-attention in artificial neural networks. Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie |
| 2020 | Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss. Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang |
| 2020 | User-Dependent Neural Sequence Models for Continuous-Time Event Data. Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth |
| 2020 | Using noise to probe recurrent neural network structure and prune synapses. Eli Moore, Rishidev Chaudhuri |
| 2020 | VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang |
| 2020 | VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar |
| 2020 | Value-driven Hindsight Modelling. Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess |
| 2020 | VarGrad: A Low-Variance Gradient Estimator for Variational Inference. Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz |
| 2020 | Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization. Chaobing Song, Yong Jiang, Yi Ma |
| 2020 | Variance reduction for Random Coordinate Descent-Langevin Monte Carlo. Zhiyan Ding, Qin Li |
| 2020 | Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. Shaocong Ma, Yi Zhou, Shaofeng Zou |
| 2020 | Variational Amodal Object Completion. Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler |
| 2020 | Variational Bayesian Monte Carlo with Noisy Likelihoods. Luigi Acerbi |
| 2020 | Variational Bayesian Unlearning. Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2020 | Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. Pantelis Elinas, Edwin V. Bonilla, Louis C. Tiao |
| 2020 | Variational Interaction Information Maximization for Cross-domain Disentanglement. HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim |
| 2020 | Variational Policy Gradient Method for Reinforcement Learning with General Utilities. Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang |
| 2020 | Video Frame Interpolation without Temporal Priors. Youjian Zhang, Chaoyue Wang, Dacheng Tao |
| 2020 | Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement. Yongqing Liang, Xin Li, Navid H. Jafari, Jim Chen |
| 2020 | WOR and Edith Cohen, Rasmus Pagh, David P. Woodruff |
| 2020 | Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization. Hassan Mortagy, Swati Gupta, Sebastian Pokutta |
| 2020 | Walsh-Hadamard Variational Inference for Bayesian Deep Learning. Simone Rossi, Sébastien Marmin, Maurizio Filippone |
| 2020 | Wasserstein Distances for Stereo Disparity Estimation. Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao |
| 2020 | Watch out! Motion is Blurring the Vision of Your Deep Neural Networks. Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu |
| 2020 | Wavelet Flow: Fast Training of High Resolution Normalizing Flows. Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker |
| 2020 | Weak Form Generalized Hamiltonian Learning. Kevin Course, Trefor W. Evans, Prasanth B. Nair |
| 2020 | Weakly Supervised Deep Functional Maps for Shape Matching. Abhishek Sharma, Maks Ovsjanikov |
| 2020 | Weakly-Supervised Reinforcement Learning for Controllable Behavior. Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn |
| 2020 | Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson |
| 2020 | Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. Christopher Morris, Gaurav Rattan, Petra Mutzel |
| 2020 | Weston-Watkins Hinge Loss and Ordered Partitions. Yutong Wang, Clayton Scott |
| 2020 | What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes. Herman Yau, Chris Russell, Simon Hadfield |
| 2020 | What Do Neural Networks Learn When Trained With Random Labels? Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers |
| 2020 | What Makes for Good Views for Contrastive Learning? Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola |
| 2020 | What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. Vitaly Feldman, Chiyuan Zhang |
| 2020 | What if Neural Networks had SVDs? Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin |
| 2020 | What is being transferred in transfer learning? Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang |
| 2020 | What shapes feature representations? Exploring datasets, architectures, and training. Katherine L. Hermann, Andrew K. Lampinen |
| 2020 | What went wrong and when? Instance-wise feature importance for time-series black-box models. Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg |
| 2020 | When Counterpoint Meets Chinese Folk Melodies. Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang |
| 2020 | When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari |
| 2020 | When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes. Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar |
| 2020 | Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao |
| 2020 | Why Normalizing Flows Fail to Detect Out-of-Distribution Data. Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson |
| 2020 | Why are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra |
| 2020 | Winning the Lottery with Continuous Sparsification. Pedro Savarese, Hugo Silva, Michael Maire |
| 2020 | Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Ahmed K. Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav V. Marathe |
| 2020 | WoodFisher: Efficient Second-Order Approximation for Neural Network Compression. Sidak Pal Singh, Dan Alistarh |
| 2020 | Woodbury Transformations for Deep Generative Flows. You Lu, Bert Huang |
| 2020 | Worst-Case Analysis for Randomly Collected Data. Justin Y. Chen, Gregory Valiant, Paul Valiant |
| 2020 | X-CAL: Explicit Calibration for Survival Analysis. Mark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath |
| 2020 | Your Classifier can Secretly Suffice Multi-Source Domain Adaptation. Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, Venkatesh Babu R. |
| 2020 | Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling. Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio |
| 2020 | Zap Q-Learning With Nonlinear Function Approximation. Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Busic, Sean P. Meyn |
| 2020 | Zero-Resource Knowledge-Grounded Dialogue Generation. Linxiao Li, Can Xu, Wei Wu, Yufan Zhao, Xueliang Zhao, Chongyang Tao |
| 2020 | f-Divergence Variational Inference. Neng Wan, Dapeng Li, Naira Hovakimyan |
| 2020 | f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning. Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang |
| 2020 | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael Auli |