ICML - RankMe – RankMe

1086 papers

Year	Title / Authors
2020	"Other-Play" for Zero-Shot Coordination. Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob N. Foerster
2020	(Locally) Differentially Private Combinatorial Semi-Bandits. Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang
2020	A Chance-Constrained Generative Framework for Sequence Optimization. Xianggen Liu, Qiang Liu, Sen Song, Jian Peng
2020	A Distributional Framework For Data Valuation. Amirata Ghorbani, Michael P. Kim, James Zou
2020	A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. Pan Xu, Quanquan Gu
2020	A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning. Yunhua Xiang, Noah Simon
2020	A Flexible Latent Space Model for Multilayer Networks. Xuefei Zhang, Songkai Xue, Ji Zhu
2020	A Free-Energy Principle for Representation Learning. Yansong Gao, Pratik Chaudhari
2020	A Game Theoretic Framework for Model Based Reinforcement Learning. Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
2020	A Generative Model for Molecular Distance Geometry. Gregor N. C. Simm, José Miguel Hernández-Lobato
2020	A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton. Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
2020	A Geometric Approach to Archetypal Analysis via Sparse Projections. Vinayak Abrol, Pulkit Sharma
2020	A Graph to Graphs Framework for Retrosynthesis Prediction. Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang
2020	A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change. Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen
2020	A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth. Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying
2020	A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
2020	A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model. Peng Wang, Zirui Zhou, Anthony Man-Cho So
2020	A Pairwise Fair and Community-preserving Approach to k-Center Clustering. Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas
2020	A Quantile-based Approach for Hyperparameter Transfer Learning. David Salinas, Huibin Shen, Valerio Perrone
2020	A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora
2020	A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition. Anurag Kumar, Vamsi K. Ithapu
2020	A Simple Framework for Contrastive Learning of Visual Representations. Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton
2020	A Swiss Army Knife for Minimax Optimal Transport. Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban
2020	A Tree-Structured Decoder for Image-to-Markup Generation. Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Lirong Dai
2020	A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
2020	A distributional view on multi-objective policy optimization. Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller
2020	A general recurrent state space framework for modeling neural dynamics during decision-making. David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
2020	A new regret analysis for Adam-type algorithms. Ahmet Alacaoglu, Yura Malitsky, Panayotis Mertikopoulos, Volkan Cevher
2020	A simpler approach to accelerated optimization: iterative averaging meets optimism. Pooria Joulani, Anant Raj, András György, Csaba Szepesvári
2020	ACFlow: Flow Models for Arbitrary Conditional Likelihoods. Yang Li, Shoaib Akbar, Junier Oliva
2020	AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation. Jae Hyun Lim, Aaron C. Courville, Christopher J. Pal, Chin-Wei Huang
2020	Abstraction Mechanisms Predict Generalization in Deep Neural Networks. Alex Gain, Hava T. Siegelmann
2020	Accelerated Message Passing for Entropy-Regularized MAP Inference. Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	Accelerated Stochastic Gradient-free and Projection-free Methods. Feihu Huang, Lue Tao, Songcan Chen
2020	Accelerating Large-Scale Inference with Anisotropic Vector Quantization. Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar
2020	Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. Futoshi Futami, Issei Sato, Masashi Sugiyama
2020	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik
2020	Acceleration through spectral density estimation. Fabian Pedregosa, Damien Scieur
2020	Accountable Off-Policy Evaluation With Kernel Bellman Statistics. Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
2020	Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates
2020	Active World Model Learning with Progress Curiosity. Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins
2020	AdaScale SGD: A User-Friendly Algorithm for Distributed Training. Tyler B. Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin
2020	Adaptive Adversarial Multi-task Representation Learning. Yuren Mao, Weiwei Liu, Xuemin Lin
2020	Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE. Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James S. Duncan
2020	Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning. Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard B. Fair
2020	Adaptive Estimator Selection for Off-Policy Evaluation. Yi Su, Pavithra Srinath, Akshay Krishnamurthy
2020	Adaptive Gradient Descent without Descent. Yura Malitsky, Konstantin Mishchenko
2020	Adaptive Region-Based Active Learning. Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
2020	Adaptive Reward-Poisoning Attacks against Reinforcement Learning. Xuezhou Zhang, Yuzhe Ma, Adish Singla, Xiaojin Zhu
2020	Adaptive Sampling for Estimating Probability Distributions. Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh
2020	Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition. Alex Gittens, Kareem S. Aggour, Bülent Yener
2020	Adding seemingly uninformative labels helps in low data regimes. Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith
2020	Adversarial Attacks on Copyright Detection Systems. Parsa Saadatpanah, Ali Shafahi, Tom Goldstein
2020	Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev
2020	Adversarial Filters of Dataset Biases. Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi
2020	Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks. Pranjal Awasthi, Natalie Frank, Mehryar Mohri
2020	Adversarial Mutual Information for Text Generation. Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura, Zhongming Jin, Xian-Sheng Hua, Deng Cai, Bo Li
2020	Adversarial Neural Pruning with Latent Vulnerability Suppression. Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
2020	Adversarial Nonnegative Matrix Factorization. Lei Luo, Yanfu Zhang, Heng Huang
2020	Adversarial Risk via Optimal Transport and Optimal Couplings. Muni Sreenivas Pydi, Varun S. Jog
2020	Adversarial Robustness Against the Union of Multiple Perturbation Models. Pratyush Maini, Eric Wong, J. Zico Kolter
2020	Adversarial Robustness for Code. Pavol Bielik, Martin T. Vechev
2020	Adversarial Robustness via Runtime Masking and Cleansing. Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu
2020	Agent57: Outperforming the Atari Human Benchmark. Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Charles Blundell
2020	Aggregation of Multiple Knockoffs. Tuan-Binh Nguyen, Jérôme-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot
2020	Aligned Cross Entropy for Non-Autoregressive Machine Translation. Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy
2020	All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan
2020	Alleviating Privacy Attacks via Causal Learning. Shruti Tople, Amit Sharma, Aditya Nori
2020	Almost Tune-Free Variance Reduction. Bingcong Li, Lingda Wang, Georgios B. Giannakis
2020	Amortised Learning by Wake-Sleep. Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani
2020	Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams
2020	Amortized Population Gibbs Samplers with Neural Sufficient Statistics. Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
2020	An Accelerated DFO Algorithm for Finite-sum Convex Functions. Yuwen Chen, Antonio Orvieto, Aurélien Lucchi
2020	An EM Approach to Non-autoregressive Conditional Sequence Generation. Zhiqing Sun, Yiming Yang
2020	An Explicitly Relational Neural Network Architecture. Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David G. T. Barrett, Marta Garnelo
2020	An Imitation Learning Approach for Cache Replacement. Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
2020	An Investigation of Why Overparameterization Exacerbates Spurious Correlations. Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
2020	An Optimistic Perspective on Offline Reinforcement Learning. Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
2020	An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang
2020	An end-to-end approach for the verification problem: learning the right distance. João Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago H. Falk
2020	Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks. Jiabao Lei, Kui Jia
2020	Anderson Acceleration of Proximal Gradient Methods. Vien V. Mai, Mikael Johansson
2020	Angular Visual Hardness. Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar
2020	Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network. Javier Turek, Shailee Jain, Vy A. Vo, Mihai Capota, Alexander Huth, Theodore L. Willke
2020	Approximation Capabilities of Neural ODEs and Invertible Residual Networks. Han Zhang, Xi Gao, Jacob Unterman, Tom Arodz
2020	Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions. Kaito Fujii
2020	Associative Memory in Iterated Overparameterized Sigmoid Autoencoders. Yibo Jiang, Cengiz Pehlevan
2020	Asynchronous Coagent Networks. James E. Kostas, Chris Nota, Philip S. Thomas
2020	Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli
2020	Attentive Group Equivariant Convolutional Networks. David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2020	AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks. Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang
2020	AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. Esteban Real, Chen Liang, David R. So, Quoc V. Le
2020	Automated Synthetic-to-Real Generalization. Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar
2020	Automatic Reparameterisation of Probabilistic Programs. Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
2020	Automatic Shortcut Removal for Self-Supervised Representation Learning. Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
2020	BINOCULARS for efficient, nonmyopic sequential experimental design. Shali Jiang, Henry Chai, Javier González, Roman Garnett
2020	Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning. Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock
2020	Bandits for BMO Functions. Tianyu Wang, Cynthia Rudin
2020	Bandits with Adversarial Scaling. Thodoris Lykouris, Vahab S. Mirrokni, Renato Paes Leme
2020	Batch Reinforcement Learning with Hyperparameter Gradients. Byung-Jun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-Eung Kim
2020	Batch Stationary Distribution Estimation. Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans
2020	Bayesian Differential Privacy for Machine Learning. Aleksei Triastcyn, Boi Faltings
2020	Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. Steven Kleinegesse, Michael U. Gutmann
2020	Bayesian Graph Neural Networks with Adaptive Connection Sampling. Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian
2020	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances. Csaba Tóth, Harald Oberhauser
2020	Bayesian Optimisation over Multiple Continuous and Categorical Inputs. Bin Xin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J. Roberts
2020	Bayesian Sparsification of Deep C-valued Networks. Ivan Nazarov, Evgeny Burnaev
2020	Being Bayesian about Categorical Probability. Taejong Joo, Uijung Chung, Min-Gwan Seo
2020	Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2020	Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting. Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
2020	Better depth-width trade-offs for neural networks through the lens of dynamical systems. Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas
2020	Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
2020	Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels. Lu Jiang, Di Huang, Mason Liu, Weilong Yang
2020	Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. Dylan J. Foster, Alexander Rakhlin
2020	Bidirectional Model-based Policy Optimization. Hang Lai, Jian Shen, Weinan Zhang, Yong Yu
2020	Bio-Inspired Hashing for Unsupervised Similarity Search. Chaitanya K. Ryali, John J. Hopfield, Leopold Grinberg, Dmitry Krotov
2020	Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer. Anton Zhiyanov, Alexey Drutsa
2020	Black-Box Methods for Restoring Monotonicity. Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos
2020	Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. Matthew D. Hoffman, Yian Ma
2020	Black-box Certification and Learning under Adversarial Perturbations. Hassan Ashtiani, Vinayak Pathak, Ruth Urner
2020	BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates. Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao, Donald K. K. Lee
2020	Boosted Histogram Transform for Regression. Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin
2020	Boosting Deep Neural Network Efficiency with Dual-Module Inference. Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie
2020	Boosting Frank-Wolfe by Chasing Gradients. Cyrille W. Combettes, Sebastian Pokutta
2020	Boosting for Control of Dynamical Systems. Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu
2020	Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, Mohammad Gheshlaghi Azar
2020	Born-Again Tree Ensembles. Thibaut Vidal, Maximilian Schiffer
2020	Bounding the fairness and accuracy of classifiers from population statistics. Sivan Sabato, Elad Yom-Tov
2020	Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
2020	Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search. Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan
2020	Bridging the Gap Between f-GANs and Wasserstein GANs. Jiaming Song, Stefano Ermon
2020	Budgeted Online Influence Maximization. Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko
2020	CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page
2020	CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information. Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, Lawrence Carin
2020	CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Michael Laskin, Aravind Srinivas, Pieter Abbeel
2020	Calibration, Entropy Rates, and Memory in Language Models. Mark Braverman, Xinyi Chen, Sham M. Kakade, Karthik Narasimhan, Cyril Zhang, Yi Zhang
2020	Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
2020	Can Increasing Input Dimensionality Improve Deep Reinforcement Learning? Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski
2020	Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? Hongchang Gao, Heng Huang
2020	Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health. Liangyu Zhu, Wenbin Lu, Rui Song
2020	Causal Effect Identifiability under Partial-Observability. Sanghack Lee, Elias Bareinboim
2020	Causal Inference using Gaussian Processes with Structured Latent Confounders. Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
2020	Causal Modeling for Fairness In Dynamical Systems. Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2020	Causal Strategic Linear Regression. Yonadav Shavit, Benjamin L. Edelman, Brian Axelrod
2020	Causal Structure Discovery from Distributions Arising from Mixtures of DAGs. Basil Saeed, Snigdha Panigrahi, Caroline Uhler
2020	Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
2020	Certified Data Removal from Machine Learning Models. Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten
2020	Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, J. Zico Kolter
2020	Channel Equilibrium Networks for Learning Deep Representation. Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo
2020	Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang
2020	Choice Set Optimization Under Discrete Choice Models of Group Decisions. Kiran Tomlinson, Austin R. Benson
2020	Circuit-Based Intrinsic Methods to Detect Overfitting. Satrajit Chatterjee, Alan Mishchenko
2020	Class-Weighted Classification: Trade-offs and Robust Approaches. Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
2020	Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. Shengpu Tang, Aditya Modi, Michael W. Sjoding, Jenna Wiens
2020	Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu
2020	Closing the convergence gap of SGD without replacement. Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos
2020	CoMic: Complementary Task Learning & Mimicry for Reusable Skills. Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel
2020	Collaborative Machine Learning with Incentive-Aware Model Rewards. Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
2020	Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems. Zhe Dong, Bryan A. Seybold, Kevin Murphy, Hung H. Bui
2020	Combinatorial Pure Exploration for Dueling Bandit. Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao
2020	Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter
2020	Communication-Efficient Distributed PCA by Riemannian Optimization. Long-Kai Huang, Sinno Jialin Pan
2020	Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
2020	Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie
2020	Composable Sketches for Functions of Frequencies: Beyond the Worst Case. Edith Cohen, Ofir Geri, Rasmus Pagh
2020	Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation. Reinhard Heckel, Mahdi Soltanolkotabi
2020	Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model. Ying Jin, Zhaoran Wang, Junwei Lu
2020	ConQUR: Mitigating Delusional Bias in Deep Q-Learning. DiJia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier
2020	Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions. Prashanth L. A., Krishna P. Jagannathan, Ravi Kumar Kolla
2020	Concept Bottleneck Models. Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
2020	Concise Explanations of Neural Networks using Adversarial Training. Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, Somesh Jha
2020	Conditional gradient methods for stochastically constrained convex minimization. Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher
2020	Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting. Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee
2020	Confidence-Aware Learning for Deep Neural Networks. Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang
2020	Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. David Stutz, Matthias Hein, Bernt Schiele
2020	Consistent Estimators for Learning to Defer to an Expert. Hussein Mozannar, David A. Sontag
2020	Consistent Structured Prediction with Max-Min Margin Markov Networks. Alex Nowak, Francis R. Bach, Alessandro Rudi
2020	Constant Curvature Graph Convolutional Networks. Gregor Bachmann, Gary Bécigneul, Octavian Ganea
2020	Constrained Markov Decision Processes via Backward Value Functions. Harsh Satija, Philip Amortila, Joelle Pineau
2020	Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks. Dmytro Perekrestenko, Stephan Müller, Helmut Bölcskei
2020	Context Aware Local Differential Privacy. Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
2020	Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning. Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin
2020	Continuous Graph Neural Networks. Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
2020	Continuous Time Bayesian Networks with Clocks. Nicolai Engelmann, Dominik Linzner, Heinz Koeppl
2020	Continuous-time Lower Bounds for Gradient-based Algorithms. Michael Muehlebach, Michael I. Jordan
2020	Continuously Indexed Domain Adaptation. Hao Wang, Hao He, Dina Katabi
2020	Contrastive Multi-View Representation Learning on Graphs. Kaveh Hassani, Amir Hosein Khas Ahmadi
2020	Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning. Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli
2020	ControlVAE: Controllable Variational Autoencoder. Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek F. Abdelzaher
2020	Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry P. Vetrov
2020	Convergence Rates of Variational Inference in Sparse Deep Learning. Badr-Eddine Chérief-Abdellatif
2020	Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization. Vien V. Mai, Mikael Johansson
2020	Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games. Youzhi Zhang, Bo An
2020	Convex Calibrated Surrogates for the Multi-Label F-Measure. Mingyuan Zhang, Harish Guruprasad Ramaswamy, Shivani Agarwal
2020	Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
2020	Convolutional Kernel Networks for Graph-Structured Data. Dexiong Chen, Laurent Jacob, Julien Mairal
2020	Convolutional dictionary learning based auto-encoders for natural exponential-family distributions. Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba
2020	Cooperative Multi-Agent Bandits with Heavy Tails. Abhimanyu Dubey, Alex 'Sandy' Pentland
2020	Coresets for Clustering in Graphs of Bounded Treewidth. Daniel N. Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2020	Coresets for Data-efficient Training of Machine Learning Models. Baharan Mirzasoleiman, Jeff A. Bilmes, Jure Leskovec
2020	Correlation Clustering with Asymmetric Classification Errors. Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev
2020	Cost-Effective Interactive Attention Learning with Neural Attention Processes. Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang
2020	Cost-effectively Identifying Causal Effects When Only Response Variable is Observable. Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou
2020	Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. Yuta Saito, Shota Yasui
2020	Countering Language Drift with Seeded Iterated Learning. Yuchen Lu, Soumye Singhal, Florian Strub, Aaron C. Courville, Olivier Pietquin
2020	Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi
2020	Curvature-corrected learning dynamics in deep neural networks. Dongsung Huh
2020	Customizing ML Predictions for Online Algorithms. Keerti Anand, Rong Ge, Debmalya Panigrahi
2020	DINO: Distributed Newton-Type Optimization Method. Rixon Crane, Fred Roosta
2020	DROCC: Deep Robust One-Class Classification. Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
2020	DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images. Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker
2020	Data Amplification: Instance-Optimal Property Estimation. Yi Hao, Alon Orlitsky
2020	Data Valuation using Reinforcement Learning. Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister
2020	Data preprocessing to mitigate bias: A maximum entropy based approach. L. Elisa Celis, Vijay Keswani, Nisheeth K. Vishnoi
2020	Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models. Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
2020	Data-Efficient Image Recognition with Contrastive Predictive Coding. Olivier J. Hénaff
2020	DeBayes: a Bayesian Method for Debiasing Network Embeddings. Maarten Buyl, Tijl De Bie
2020	Debiased Sinkhorn barycenters. Hicham Janati, Marco Cuturi, Alexandre Gramfort
2020	Decentralised Learning with Random Features and Distributed Gradient Descent. Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
2020	Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine
2020	Decision Trees for Decision-Making under the Predict-then-Optimize Framework. Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
2020	Decoupled Greedy Learning of CNNs. Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2020	Deep Coordination Graphs. Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson
2020	Deep Divergence Learning. Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis
2020	Deep Gaussian Markov Random Fields. Per Sidén, Fredrik Lindsten
2020	Deep Graph Random Process for Relational-Thinking-Based Speech Recognition. Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang
2020	Deep Isometric Learning for Visual Recognition. Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik
2020	Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks. Marko Vasic, Cameron T. Chalk, Sarfraz Khurshid, David Soloveichik
2020	Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions. Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, Ronnie Sircar
2020	Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning. Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
2020	Deep Reinforcement Learning with Robust and Smooth Policy. Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao
2020	Deep Streaming Label Learning. Zhen Wang, Liu Liu, Dacheng Tao
2020	Deep k-NN for Noisy Labels. Dara Bahri, Heinrich Jiang, Maya R. Gupta
2020	DeepCoDA: personalized interpretability for compositional health data. Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh
2020	DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. Nathan Kallus
2020	Defense Through Diverse Directions. Christopher M. Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier Oliva
2020	DeltaGrad: Rapid retraining of machine learning models. Yinjun Wu, Edgar Dobriban, Susan B. Davidson
2020	Description Based Text Classification with Reinforcement Learning. Duo Chai, Wei Wu, Qinghong Han, Fei Wu, Jiwei Li
2020	Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach. Junzhe Zhang
2020	DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths. Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, Yuan Yao
2020	Detecting Out-of-Distribution Examples with Gram Matrices. Chandramouli Shama Sastry, Sageev Oore
2020	Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
2020	Differentiable Product Quantization for End-to-End Embedding Compression. Ting Chen, Lala Li, Yizhou Sun
2020	Differentially Private Set Union. Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
2020	Differentiating through the Fréchet Mean. Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa
2020	Discount Factor as a Regularizer in Reinforcement Learning. Ron Amit, Ron Meir, Kamil Ciosek
2020	Discriminative Adversarial Search for Abstractive Summarization. Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
2020	Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions. Ahmed M. Alaa, Mihaela van der Schaar
2020	Disentangling Trainability and Generalization in Deep Neural Networks. Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz
2020	Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation. Wenxian Shi, Hao Zhou, Ning Miao, Lei Li
2020	Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation. Konstantinos Pitas
2020	Distance Metric Learning with Joint Representation Diversification. Xu Chu, Yang Lin, Yasha Wang, Xiting Wang, Hailong Yu, Xin Gao, Qi Tong
2020	Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
2020	Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent. Nima Eshraghi, Ben Liang
2020	Distribution Augmentation for Generative Modeling. Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
2020	Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits. Nian Si, Fan Zhang, Zhengyuan Zhou, Jose H. Blanchet
2020	Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh
2020	Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
2020	Do GANs always have Nash equilibria? Farzan Farnia, Asuman E. Ozdaglar
2020	Do RNN and LSTM have Long Memory? Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian
2020	Do We Need Zero Training Loss After Achieving Zero Training Error? Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation. Jian Liang, Dapeng Hu, Jiashi Feng
2020	Does label smoothing mitigate label noise? Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar
2020	Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making. Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng
2020	Domain Adaptive Imitation Learning. Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
2020	Domain Aggregation Networks for Multi-Source Domain Adaptation. Junfeng Wen, Russell Greiner, Dale Schuurmans
2020	Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript. Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui
2020	Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. Nathan Kallus, Masatoshi Uehara
2020	Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime. Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala
2020	Double-Loop Unadjusted Langevin Algorithm. Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher
2020	Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. Qi Wang, Herke van Hoof
2020	Doubly robust off-policy evaluation with shrinkage. Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík
2020	DropNet: Reducing Neural Network Complexity via Iterative Pruning. Chong Min John Tan, Mehul Motani
2020	Dual Mirror Descent for Online Allocation Problems. Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni
2020	Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian
2020	Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses. Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc
2020	Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising. Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai
2020	Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. Jiaoyang Huang, Horng-Tzer Yau
2020	ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications. Kinjal Basu, Amol Ghoting, Rahul Mazumder, Yao Pan
2020	Educating Text Autoencoders: Latent Representation Guidance via Denoising. Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola
2020	Efficient Continuous Pareto Exploration in Multi-Task Learning. Pingchuan Ma, Tao Du, Wojciech Matusik
2020	Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
2020	Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. Daniel Kumor, Carlos Cinelli, Elias Bareinboim
2020	Efficient Intervention Design for Causal Discovery with Latents. Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco
2020	Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow
2020	Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation. Marc Abeille, Alessandro Lazaric
2020	Efficient Policy Learning from Surrogate-Loss Classification Reductions. Andrew Bennett, Nathan Kallus
2020	Efficient Proximal Mapping of the 1-path-norm of Shallow Networks. Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher
2020	Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann
2020	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	Efficient nonparametric statistical inference on population feature importance using Shapley values. Brian D. Williamson, Jean Feng
2020	Efficiently Learning Adversarially Robust Halfspaces with Noise. Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
2020	Efficiently Solving MDPs with Stochastic Mirror Descent. Yujia Jin, Aaron Sidford
2020	Efficiently sampling functions from Gaussian process posteriors. James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
2020	Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani
2020	Eliminating the Invariance on the Loss Landscape of Linear Autoencoders. Reza Oftadeh, Jiayi Shen, Zhangyang Wang, Dylan A. Shell
2020	Emergence of Separable Manifolds in Deep Language Representations. Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung
2020	Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag
2020	Encoding Musical Style with Transformer Autoencoders. Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse H. Engel
2020	Energy-Based Processes for Exchangeable Data. Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans
2020	Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions. Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, Kenneth O. Stanley
2020	Enhancing Simple Models by Exploiting What They Already Know. Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
2020	Entropy Minimization In Emergent Languages. Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni
2020	Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. Jonas Köhler, Leon Klein, Frank Noé
2020	Equivariant Neural Rendering. Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Josh M. Susskind, Qi Shan
2020	Error Estimation for Sketched SVD via the Bootstrap. Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
2020	Error-Bounded Correction of Noisy Labels. Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris N. Metaxas, Chao Chen
2020	Estimating Generalization under Distribution Shifts via Domain-Invariant Representations. Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka
2020	Estimating Model Uncertainty of Neural Networks in Sparse Information Form. Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
2020	Estimating Q(s,s') with Deep Deterministic Dynamics Gradients. Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski
2020	Estimating the Error of Randomized Newton Methods: A Bootstrap Approach. Jessie X. T. Chen, Miles E. Lopes
2020	Estimating the Number and Effect Sizes of Non-null Hypotheses. Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
2020	Estimation of Bounds on Potential Outcomes For Decision Making. Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
2020	Evaluating Lossy Compression Rates of Deep Generative Models. Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse
2020	Evaluating Machine Accuracy on ImageNet. Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt
2020	Evaluating the Performance of Reinforcement Learning Algorithms. Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
2020	Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination. Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen McAleer, Kagan Tumer
2020	Evolutionary Topology Search for Tensor Network Decomposition. Chao Li, Zhun Sun
2020	Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms. Chaosheng Dong, Bo Zeng
2020	Explainable and Discourse Topic-aware Neural Language Understanding. Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
2020	Explainable k-Means and k-Medians Clustering. Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost
2020	Explaining Groups of Points in Low-Dimensional Representations. Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
2020	Explicit Gradient Learning for Black-Box Optimization. Elad Sarafian, Mor Sinay, Yoram Louzoun, Noa Agmon, Sarit Kraus
2020	Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits. Xi Liu, Ping-Chun Hsieh, Yu-Heng Hung, Anirban Bhattacharya, P. R. Kumar
2020	Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills. Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giró-i-Nieto, Jordi Torres
2020	Extra-gradient with player sampling for faster convergence in n-player games. Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna
2020	Extrapolation for Large-batch Training in Deep Learning. Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
2020	Extreme Multi-label Classification from Aggregated Labels. Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon
2020	FACT: A Diagnostic for Group Fairness Trade-offs. Joon Sik Kim, Jiahao Chen, Ameet Talwalkar
2020	FR-Train: A Mutual Information-Based Approach to Fair and Robust Training. Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
2020	Fair Generative Modeling via Weak Supervision. Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
2020	Fair Learning with Private Demographic Data. Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
2020	Fair k-Centers via Maximum Matching. Matthew Jones, Huy L. Nguyen, Thy Dinh Nguyen
2020	Fairwashing explanations with off-manifold detergent. Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel
2020	Familywise Error Rate Control by Interactive Unmasking. Boyan Duan, Aaditya Ramdas, Larry A. Wasserman
2020	Fast Adaptation to New Environments via Policy-Dynamics Value Functions. Roberta Raileanu, Maxwell Goldstein, Arthur Szlam, Rob Fergus
2020	Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima
2020	Fast Differentiable Sorting and Ranking. Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga
2020	Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case. Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
2020	Fast OSCAR and OWL Regression via Safe Screening Rules. Runxue Bao, Bin Gu, Heng Huang
2020	Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations. Robert Mattila, Cristian R. Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg
2020	Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints. Akbar Rafiey, Yuichi Yoshida
2020	Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods. Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré
2020	Fast computation of Nash Equilibria in Imperfect Information Games. Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls
2020	Faster Graph Embeddings via Coarsening. Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang
2020	Feature Noise Induces Loss Discrepancy Across Groups. Fereshte Khani, Percy Liang
2020	Feature Quantization Improves GAN Training. Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, Changyou Chen
2020	Feature Selection using Stochastic Gates. Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger
2020	Feature-map-level Online Adversarial Knowledge Distillation. Inseop Chung, Seonguk Park, Jangho Kim, Nojun Kwak
2020	FedBoost: A Communication-Efficient Algorithm for Federated Learning. Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh
2020	Federated Learning with Only Positive Labels. Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar
2020	FetchSGD: Communication-Efficient Federated Learning with Sketching. Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora
2020	Few-shot Domain Adaptation by Causal Mechanism Transfer. Takeshi Teshima, Issei Sato, Masashi Sugiyama
2020	Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs. Meng Qu, Tianyu Gao, Louis-Pascal A. C. Xhonneux, Jian Tang
2020	Fiduciary Bandits. Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
2020	Fiedler Regularization: Learning Neural Networks with Graph Sparsity. Edric Tam, David B. Dunson
2020	Finding trainable sparse networks through Neural Tangent Transfer. Tianlin Liu, Friedemann Zenke
2020	Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent. Yunwen Lei, Yiming Ying
2020	Finite-Time Convergence in Continuous-Time Optimization. Orlando Romero, Mouhacine Benosman
2020	Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games. Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael I. Jordan
2020	Flexible and Efficient Long-Range Planning Through Curious Exploration. Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
2020	Forecasting Sequential Data Using Consistent Koopman Autoencoders. Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney
2020	FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis. Aman Sinha, Matthew O'Kelly, Hongrui Zheng, Rahul Mangharam, John C. Duchi, Russ Tedrake
2020	Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos. Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
2020	Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban
2020	Frequency Bias in Neural Networks for Input of Non-Uniform Density. Ronen Basri, Meirav Galun, Amnon Geifman, David W. Jacobs, Yoni Kasten, Shira Kritchman
2020	Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. Ahmed M. Alaa, Mihaela van der Schaar
2020	From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics. Sai Ganesh Nagarajan, David Balduzzi, Georgios Piliouras
2020	From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry
2020	From Importance Sampling to Doubly Robust Policy Gradient. Jiawei Huang, Nan Jiang
2020	From Local SGD to Local Fixed-Point Methods for Federated Learning. Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik
2020	From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. Aadirupa Saha, Aditya Gopalan
2020	From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause
2020	Frustratingly Simple Few-Shot Object Detection. Xin Wang, Thomas E. Huang, Joseph Gonzalez, Trevor Darrell, Fisher Yu
2020	Full Law Identification in Graphical Models of Missing Data: Completeness Results. Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
2020	Fully Parallel Hyperparameter Search: Reshaped Space-Filling. Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jérémy Rapin, Morgane Rivière, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier
2020	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
2020	GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation. Marc Brockschmidt
2020	Gamification of Pure Exploration for Linear Bandits. Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
2020	Generalisation error in learning with random features and the hidden manifold model. Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová
2020	Generalization Error of Generalized Linear Models in High Dimensions. Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
2020	Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. Liang Ding, Rui Tuo, Shahin Shahrampour
2020	Generalization and Representational Limits of Graph Neural Networks. Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola
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2020	Generalized and Scalable Optimal Sparse Decision Trees. Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer
2020	Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
2020	Generating Programmatic Referring Expressions via Program Synthesis. Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik
2020	Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate. Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang
2020	Generative Flows with Matrix Exponential. Changyi Xiao, Ligang Liu
2020	Generative Pretraining From Pixels. Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever
2020	Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, Jeffrey Clune
2020	Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models. Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov
2020	Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans
2020	Goal-Aware Prediction: Learning to Model What Matters. Suraj Nair, Silvio Savarese, Chelsea Finn
2020	Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu
2020	Goodness-of-Fit Tests for Inhomogeneous Random Graphs. Soham Dan, Bhaswar B. Bhattacharya
2020	Gradient Temporal-Difference Learning with Regularized Corrections. Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
2020	Gradient-free Online Learning in Continuous Games with Delayed Rewards. Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou
2020	GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values. Shangtong Zhang, Bo Liu, Shimon Whiteson
2020	Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. Wenhui Yu, Zheng Qin
2020	Graph Filtration Learning. Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt
2020	Graph Homomorphism Convolution. Hoang Nguyen, Takanori Maehara
2020	Graph Optimal Transport for Cross-Domain Alignment. Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu
2020	Graph Random Neural Features for Distance-Preserving Graph Representations. Daniele Zambon, Cesare Alippi, Lorenzo Livi
2020	Graph Structure of Neural Networks. Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie
2020	Graph-based Nearest Neighbor Search: From Practice to Theory. Liudmila Prokhorenkova, Aleksandr Shekhovtsov
2020	Graph-based, Self-Supervised Program Repair from Diagnostic Feedback. Michihiro Yasunaga, Percy Liang
2020	GraphOpt: Learning Optimization Models of Graph Formation. Rakshit S. Trivedi, Jiachen Yang, Hongyuan Zha
2020	Graphical Models Meet Bandits: A Variational Thompson Sampling Approach. Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel
2020	Growing Action Spaces. Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve
2020	Growing Adaptive Multi-hyperplane Machines. Nemanja Djuric, Zhuang Wang, Slobodan Vucetic
2020	Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. Rie Johnson, Tong Zhang
2020	Haar Graph Pooling. Yuguang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, Yanan Fan
2020	Hallucinative Topological Memory for Zero-Shot Visual Planning. Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar
2020	Handling the Positive-Definite Constraint in the Bayesian Learning Rule. Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan
2020	Harmonic Decompositions of Convolutional Networks. Meyer Scetbon, Zaïd Harchaoui
2020	Healing Products of Gaussian Process Experts. Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth
2020	Hierarchical Generation of Molecular Graphs using Structural Motifs. Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	Hierarchical Verification for Adversarial Robustness. Cong Han Lim, Raquel Urtasun, Ersin Yumer
2020	Hierarchically Decoupled Imitation For Morphological Transfer. Donald J. Hejna III, Lerrel Pinto, Pieter Abbeel
2020	High-dimensional Robust Mean Estimation via Gradient Descent. Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
2020	History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang
2020	How Good is the Bayes Posterior in Deep Neural Networks Really? Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020	How recurrent networks implement contextual processing in sentiment analysis. Niru Maheswaranathan, David Sussillo
2020	How to Solve Fair k-Center in Massive Data Models. Ashish Chiplunkar, Sagar Sudhir Kale, Sivaramakrishnan Natarajan Ramamoorthy
2020	How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
2020	Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization. Pan Zhou, Xiao-Tong Yuan
2020	Hypernetwork approach to generating point clouds. Przemyslaw Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zieba, Tomasz Trzcinski
2020	IPBoost - Non-Convex Boosting via Integer Programming. Marc E. Pfetsch, Sebastian Pokutta
2020	Identifying Statistical Bias in Dataset Replication. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry
2020	Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation. Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei
2020	Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability. Mingjie Li, Lingshen He, Zhouchen Lin
2020	Implicit Generative Modeling for Efficient Exploration. Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu
2020	Implicit Geometric Regularization for Learning Shapes. Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman
2020	Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study. Tanner Fiez, Benjamin Chasnov, Lillian J. Ratliff
2020	Implicit Regularization of Random Feature Models. Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel
2020	Implicit competitive regularization in GANs. Florian Schäfer, Hongkai Zheng, Animashree Anandkumar
2020	Implicit differentiation of Lasso-type models for hyperparameter optimization. Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2020	Improved Communication Cost in Distributed PageRank Computation - A Theoretical Study. Siqiang Luo
2020	Improved Optimistic Algorithms for Logistic Bandits. Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq
2020	Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. Aadirupa Saha, Pierre Gaillard, Michal Valko
2020	Improving Generative Imagination in Object-Centric World Models. Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn
2020	Improving Molecular Design by Stochastic Iterative Target Augmentation. Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola
2020	Improving Robustness of Deep-Learning-Based Image Reconstruction. Ankit Raj, Yoram Bresler, Bo Li
2020	Improving Transformer Optimization Through Better Initialization. Xiao Shi Huang, Felipe Pérez, Jimmy Ba, Maksims Volkovs
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2020	Improving the Gating Mechanism of Recurrent Neural Networks. Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu
2020	Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking. Haoran Sun, Songtao Lu, Mingyi Hong
2020	Imputer: Sequence Modelling via Imputation and Dynamic Programming. William Chan, Chitwan Saharia, Geoffrey E. Hinton, Mohammad Norouzi, Navdeep Jaitly
2020	In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
2020	Incremental Sampling Without Replacement for Sequence Models. Kensen Shi, David Bieber, Charles Sutton
2020	Individual Calibration with Randomized Forecasting. Shengjia Zhao, Tengyu Ma, Stefano Ermon
2020	Individual Fairness for k-Clustering. Sepideh Mahabadi, Ali Vakilian
2020	Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William M. Leiserson, Sage Moore, Nir Shavit, Dan Alistarh
2020	Inductive Relation Prediction by Subgraph Reasoning. Komal K. Teru, Etienne G. Denis, William L. Hamilton
2020	Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters. Subho S. Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar K. Iyer
2020	Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization. Hien Le, Nicolas Gillis, Panagiotis Patrinos
2020	Inexact Tensor Methods with Dynamic Accuracies. Nikita Doikov, Yurii E. Nesterov
2020	Inferring DQN structure for high-dimensional continuous control. Andrey Sakryukin, Chedy Raïssi, Mohan S. Kankanhalli
2020	Infinite attention: NNGP and NTK for deep attention networks. Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak
2020	Influenza Forecasting Framework based on Gaussian Processes. Christoph Zimmer, Reza Yaesoubi
2020	InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs. Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh
2020	Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains. Johannes Fischer, Ömer Sahin Tas
2020	Information-Theoretic Local Minima Characterization and Regularization. Zhiwei Jia, Hao Su
2020	Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
2020	Input-Sparsity Low Rank Approximation in Schatten Norm. Yi Li, David P. Woodruff
2020	InstaHide: Instance-hiding Schemes for Private Distributed Learning. Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora
2020	Inter-domain Deep Gaussian Processes. Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	Interference and Generalization in Temporal Difference Learning. Emmanuel Bengio, Joelle Pineau, Doina Precup
2020	Interferometric Graph Transform: a Deep Unsupervised Graph Representation. Edouard Oyallon
2020	Interpolation between Residual and Non-Residual Networks. Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi
2020	Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions. Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez
2020	Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure. John Sipple
2020	Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge. Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu
2020	Interpreting Robust Optimization via Adversarial Influence Functions. Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang
2020	Intrinsic Reward Driven Imitation Learning via Generative Model. Xingrui Yu, Yueming Lyu, Ivor W. Tsang
2020	Invariant Causal Prediction for Block MDPs. Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
2020	Invariant Rationalization. Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola
2020	Invariant Risk Minimization Games. Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar
2020	Inverse Active Sensing: Modeling and Understanding Timely Decision-Making. Daniel Jarrett, Mihaela van der Schaar
2020	Invertible generative models for inverse problems: mitigating representation error and dataset bias. Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, Paul Hand
2020	Involutive MCMC: a Unifying Framework. Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
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2020	Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney
2020	It's Not What Machines Can Learn, It's What We Cannot Teach. Gal Yehuda, Moshe Gabel, Assaf Schuster
2020	Kernel Methods for Cooperative Multi-Agent Contextual Bandits. Abhimanyu Dubey, Alex 'Sandy' Pentland
2020	Kernel interpolation with continuous volume sampling. Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
2020	Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton
2020	Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford
2020	Knowing The What But Not The Where in Bayesian Optimization. Vu Nguyen, Michael A. Osborne
2020	LEEP: A New Measure to Evaluate Transferability of Learned Representations. Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau
2020	LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction. Vlad Niculae, André F. T. Martins
2020	LTF: A Label Transformation Framework for Correcting Label Shift. Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao
2020	Label-Noise Robust Domain Adaptation. Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao
2020	Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks. Alexander Shevchenko, Marco Mondelli
2020	Laplacian Regularized Few-Shot Learning. Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed
2020	Latent Bernoulli Autoencoder. Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, Paolo Remagnino
2020	Latent Space Factorisation and Manipulation via Matrix Subspace Projection. Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin
2020	Latent Variable Modelling with Hyperbolic Normalizing Flows. Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton
2020	Layered Sampling for Robust Optimization Problems. Hu Ding, Zixiu Wang
2020	LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments. Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash
2020	Learnable Group Transform For Time-Series. Romain Cosentino, Behnaam Aazhang
2020	Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
2020	Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization. Sicheng Zhu, Xiao Zhang, David Evans
2020	Learning Algebraic Multigrid Using Graph Neural Networks. Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh
2020	Learning Autoencoders with Relational Regularization. Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin
2020	Learning Calibratable Policies using Programmatic Style-Consistency. Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, Matthew J. Hausknecht
2020	Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning. Sang-Hyun Lee, Seung-Woo Seo
2020	Learning De-biased Representations with Biased Representations. Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh
2020	Learning Deep Kernels for Non-Parametric Two-Sample Tests. Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
2020	Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information. Karl Stratos, Sam Wiseman
2020	Learning Efficient Multi-agent Communication: An Information Bottleneck Approach. Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
2020	Learning Factorized Weight Matrix for Joint Filtering. Xiangyu Xu, Yongrui Ma, Wenxiu Sun
2020	Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards. Umer Siddique, Paul Weng, Matthieu Zimmer
2020	Learning Flat Latent Manifolds with VAEs. Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt
2020	Learning Human Objectives by Evaluating Hypothetical Behavior. Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
2020	Learning Mixtures of Graphs from Epidemic Cascades. Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
2020	Learning Near Optimal Policies with Low Inherent Bellman Error. Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
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2020	Learning Optimal Tree Models under Beam Search. Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai
2020	Learning Portable Representations for High-Level Planning. Steven James, Benjamin Rosman, George Konidaris
2020	Learning Quadratic Games on Networks. Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland
2020	Learning Reasoning Strategies in End-to-End Differentiable Proving. Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
2020	Learning Representations that Support Extrapolation. Taylor W. Webb, Zachary Dulberg, Steven Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan Cohen
2020	Learning Robot Skills with Temporal Variational Inference. Tanmay Shankar, Abhinav Gupta
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2020	Learning Similarity Metrics for Numerical Simulations. Georg Kohl, Kiwon Um, Nils Thuerey
2020	Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective. Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
2020	Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion. Trong Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet
2020	Learning To Stop While Learning To Predict. Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song
2020	Learning What to Defer for Maximum Independent Sets. Sungsoo Ahn, Younggyo Seo, Jinwoo Shin
2020	Learning and Evaluating Contextual Embedding of Source Code. Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi
2020	Learning and Sampling of Atomic Interventions from Observations. Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, N. Variyam Vinodchandran
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2020	Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar
2020	Learning from Irregularly-Sampled Time Series: A Missing Data Perspective. Steven Cheng-Xian Li, Benjamin M. Marlin
2020	Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	Learning the Valuations of a k-demand Agent. Hanrui Zhang, Vincent Conitzer
2020	Learning the piece-wise constant graph structure of a varying Ising model. Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
2020	Learning to Branch for Multi-Task Learning. Pengsheng Guo, Chen-Yu Lee, Daniel Ulbricht
2020	Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
2020	Learning to Encode Position for Transformer with Continuous Dynamical Model. Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh
2020	Learning to Learn Kernels with Variational Random Features. Xiantong Zhen, Haoliang Sun, Ying-Jun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
2020	Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam M. J. Thomas, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio
2020	Learning to Rank Learning Curves. Martin Wistuba, Tejaswini Pedapati
2020	Learning to Score Behaviors for Guided Policy Optimization. Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
2020	Learning to Simulate Complex Physics with Graph Networks. Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia
2020	Learning to Simulate and Design for Structural Engineering. Kai-Hung Chang, Chin-Yi Cheng
2020	Learning with Bounded Instance and Label-dependent Label Noise. Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao
2020	Learning with Feature and Distribution Evolvable Streams. Zhenyu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou
2020	Learning with Good Feature Representations in Bandits and in RL with a Generative Model. Tor Lattimore, Csaba Szepesvári, Gellért Weisz
2020	Learning with Multiple Complementary Labels. Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
2020	Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets. Guy Hacohen, Leshem Choshen, Daphna Weinshall
2020	Leveraging Frequency Analysis for Deep Fake Image Recognition. Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz
2020	Leveraging Procedural Generation to Benchmark Reinforcement Learning. Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
2020	Lifted Disjoint Paths with Application in Multiple Object Tracking. Andrea Hornáková, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda
2020	Likelihood-free MCMC with Amortized Approximate Ratio Estimators. Joeri Hermans, Volodimir Begy, Gilles Louppe
2020	Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming. Daoli Zhu, Lei Zhao
2020	Linear Lower Bounds and Conditioning of Differentiable Games. Adam Ibrahim, Waïss Azizian, Gauthier Gidel, Ioannis Mitliagkas
2020	Linear Mode Connectivity and the Lottery Ticket Hypothesis. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	Linear bandits with Stochastic Delayed Feedback. Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner
2020	Logarithmic Regret for Adversarial Online Control. Dylan J. Foster, Max Simchowitz
2020	Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently. Asaf B. Cassel, Alon Cohen, Tomer Koren
2020	Logistic Regression for Massive Data with Rare Events. HaiYing Wang
2020	Lookahead-Bounded Q-learning. Ibrahim El Shar, Daniel R. Jiang
2020	Lorentz Group Equivariant Neural Network for Particle Physics. Alexander Bogatskiy, Brandon M. Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor
2020	Loss Function Search for Face Recognition. Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei
2020	Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. Adeel Pervez, Taco Cohen, Efstratios Gavves
2020	Low-Rank Bottleneck in Multi-head Attention Models. Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
2020	Low-Variance and Zero-Variance Baselines for Extensive-Form Games. Trevor Davis, Martin Schmid, Michael Bowling
2020	Low-loss connection of weight vectors: distribution-based approaches. Ivan Anokhin, Dmitry Yarotsky
2020	LowFER: Low-rank Bilinear Pooling for Link Prediction. Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield, Günter Neumann
2020	Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang
2020	Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. Yu-Sheng Li, Wei-Lin Chiang, Ching-pei Lee
2020	Mapping natural-language problems to formal-language solutions using structured neural representations. Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth D. Forbus, Jianfeng Gao
2020	Margin-aware Adversarial Domain Adaptation with Optimal Transport. Sofien Dhouib, Ievgen Redko, Carole Lartizien
2020	Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning. Silviu Pitis, Harris Chan, Stephen Zhao, Bradly C. Stadie, Jimmy Ba
2020	Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation. Amr Alexandari, Anshul Kundaje, Avanti Shrikumar
2020	Maximum-and-Concatenation Networks. Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin
2020	Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz
2020	Median Matrix Completion: from Embarrassment to Optimality. Weidong Liu, Xiaojun Mao, Raymond K. W. Wong
2020	Message Passing Least Squares Framework and its Application to Rotation Synchronization. Yunpeng Shi, Gilad Lerman
2020	Meta Variance Transfer: Learning to Augment from the Others. Seong-Jin Park, Seungju Han, Ji-Won Baek, Insoo Kim, Juhwan Song, Haebeom Lee, Jae-Joon Han, Sung Ju Hwang
2020	Meta-Learning with Shared Amortized Variational Inference. Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari
2020	Meta-learning for Mixed Linear Regression. Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh
2020	Meta-learning with Stochastic Linear Bandits. Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
2020	MetaFun: Meta-Learning with Iterative Functional Updates. Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh
2020	Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks. Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly
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2020	Minimax Pareto Fairness: A Multi Objective Perspective. Natalia Martínez, Martín Bertrán, Guillermo Sapiro
2020	Minimax Rate for Learning From Pairwise Comparisons in the BTL Model. Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama
2020	Minimax Weight and Q-Function Learning for Off-Policy Evaluation. Masatoshi Uehara, Jiawei Huang, Nan Jiang
2020	Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation. Yaqi Duan, Zeyu Jia, Mengdi Wang
2020	Missing Data Imputation using Optimal Transport. Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi
2020	Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning. Jize Zhang, Bhavya Kailkhura, Thomas Yong-Jin Han
2020	MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time. Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi
2020	Model Fusion with Kullback-Leibler Divergence. Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
2020	Model-Based Reinforcement Learning with Value-Targeted Regression. Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang
2020	Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes. Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Hiteshi Sharma, Rahul Jain
2020	Modulating Surrogates for Bayesian Optimization. Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek
2020	Momentum Improves Normalized SGD. Ashok Cutkosky, Harsh Mehta
2020	Momentum-Based Policy Gradient Methods. Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang
2020	Moniqua: Modulo Quantized Communication in Decentralized SGD. Yucheng Lu, Christopher De Sa
2020	Monte-Carlo Tree Search as Regularized Policy Optimization. Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models. Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi
2020	More Information Supervised Probabilistic Deep Face Embedding Learning. Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang
2020	Multi-Agent Determinantal Q-Learning. Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang
2020	Multi-Agent Routing Value Iteration Network. Quinlan Sykora, Mengye Ren, Raquel Urtasun
2020	Multi-Objective Molecule Generation using Interpretable Substructures. Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs. Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris, Christos-Savvas Bouganis
2020	Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization. Debabrata Mahapatra, Vaibhav Rajan
2020	Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
2020	Multi-objective Bayesian Optimization using Pareto-frontier Entropy. Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama
2020	Multi-step Greedy Reinforcement Learning Algorithms. Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh
2020	Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation. Georgios Smyrnis, Petros Maragos
2020	Multidimensional Shape Constraints. Maya R. Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan, Sen Zhao
2020	Multigrid Neural Memory. Tri Huynh, Michael Maire, Matthew R. Walter
2020	Multilinear Latent Conditioning for Generating Unseen Attribute Combinations. Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis
2020	Multinomial Logit Bandit with Low Switching Cost. Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou
2020	Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis. Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
2020	Mutual Transfer Learning for Massive Data. Ching-Wei Cheng, Xingye Qiao, Guang Cheng
2020	My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits. Ilai Bistritz, Tavor Z. Baharav, Amir Leshem, Nicholas Bambos
2020	NADS: Neural Architecture Distribution Search for Uncertainty Awareness. Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian
2020	NGBoost: Natural Gradient Boosting for Probabilistic Prediction. Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler
2020	Naive Exploration is Optimal for Online LQR. Max Simchowitz, Dylan J. Foster
2020	Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling. David P. Woodruff, Amir Zandieh
2020	Near-Tight Margin-Based Generalization Bounds for Support Vector Machines. Allan Grønlund, Lior Kamma, Kasper Green Larsen
2020	Near-linear time Gaussian process optimization with adaptive batching and resparsification. Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
2020	Near-optimal Regret Bounds for Stochastic Shortest Path. Aviv Rosenberg, Alon Cohen, Yishay Mansour, Haim Kaplan
2020	Near-optimal sample complexity bounds for learning Latent k-polytopes and applications to Ad-Mixtures. Chiranjib Bhattacharyya, Ravindran Kannan
2020	Nearly Linear Row Sampling Algorithm for Quantile Regression. Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang
2020	Negative Sampling in Semi-Supervised learning. John Chen, Vatsal Shah, Anastasios Kyrillidis
2020	Nested Subspace Arrangement for Representation of Relational Data. Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa
2020	NetGAN without GAN: From Random Walks to Low-Rank Approximations. Luca Rendsburg, Holger Heidrich, Ulrike von Luxburg
2020	Neural Architecture Search in A Proxy Validation Loss Landscape. Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu
2020	Neural Clustering Processes. Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski
2020	Neural Contextual Bandits with UCB-based Exploration. Dongruo Zhou, Lihong Li, Quanquan Gu
2020	Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification. Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner
2020	Neural Kernels Without Tangents. Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht
2020	Neural Network Control Policy Verification With Persistent Adversarial Perturbation. Yuh-Shyang Wang, Lily Weng, Luca Daniel
2020	Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks. Mert Pilanci, Tolga Ergen
2020	Neural Topic Modeling with Continual Lifelong Learning. Pankaj Gupta, Yatin Chaudhary, Thomas A. Runkler, Hinrich Schütze
2020	Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning". Saeed Amizadeh, Hamid Palangi, Alex Polozov, Yichen Huang, Kazuhito Koishida
2020	New Oracle-Efficient Algorithms for Private Synthetic Data Release. Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
2020	No-Regret Exploration in Goal-Oriented Reinforcement Learning. Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
2020	No-Regret and Incentive-Compatible Online Learning. Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
2020	Non-Autoregressive Neural Text-to-Speech. Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao
2020	Non-Stationary Delayed Bandits with Intermediate Observations. Claire Vernade, András György, Timothy A. Mann
2020	Non-autoregressive Machine Translation with Disentangled Context Transformer. Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu
2020	Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
2020	Non-separable Non-stationary random fields. Kangrui Wang, Oliver Hamelijnck, Theodoros Damoulas, Mark F. J. Steel
2020	Nonparametric Score Estimators. Yuhao Zhou, Jiaxin Shi, Jun Zhu
2020	Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
2020	Normalized Loss Functions for Deep Learning with Noisy Labels. Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah M. Erfani, James Bailey
2020	Normalizing Flows on Tori and Spheres. Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020	OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning. Alexander Vezhnevets, Yuhuai Wu, Maria K. Eckstein, Rémi Leblond, Joel Z. Leibo
2020	Obtaining Adjustable Regularization for Free via Iterate Averaging. Jingfeng Wu, Vladimir Braverman, Lin Yang
2020	Off-Policy Actor-Critic with Shared Experience Replay. Simon Schmitt, Matteo Hessel, Karen Simonyan
2020	On Approximate Thompson Sampling with Langevin Algorithms. Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
2020	On Breaking Deep Generative Model-based Defenses and Beyond. Yanzhi Chen, Renjie Xie, Zhanxing Zhu
2020	On Conditional Versus Marginal Bias in Multi-Armed Bandits. Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2020	On Contrastive Learning for Likelihood-free Inference. Conor Durkan, Iain Murray, George Papamakarios
2020	On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
2020	On Coresets for Regularized Regression. Rachit Chhaya, Anirban Dasgupta, Supratim Shit
2020	On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu
2020	On Efficient Constructions of Checkpoints. Yu Chen, Zhenming Liu, Bin Ren, Xin Jin
2020	On Efficient Low Distortion Ultrametric Embedding. Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde
2020	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. Tianyi Lin, Chi Jin, Michael I. Jordan
2020	On Implicit Regularization in β-VAEs. Abhishek Kumar, Ben Poole
2020	On Layer Normalization in the Transformer Architecture. Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu
2020	On Learning Language-Invariant Representations for Universal Machine Translation. Han Zhao, Junjie Hu, Andrej Risteski
2020	On Learning Sets of Symmetric Elements. Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya
2020	On Leveraging Pretrained GANs for Generation with Limited Data. Miaoyun Zhao, Yulai Cong, Lawrence Carin
2020	On Lp-norm Robustness of Ensemble Decision Stumps and Trees. Yihan Wang, Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh
2020	On Relativistic f-Divergences. Alexia Jolicoeur-Martineau
2020	On Second-Order Group Influence Functions for Black-Box Predictions. Samyadeep Basu, Xuchen You, Soheil Feizi
2020	On Semi-parametric Inference for BART. Veronika Rocková
2020	On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm. Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui
2020	On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies. Hengrui Cai, Wenbin Lu, Rui Song
2020	On Variational Learning of Controllable Representations for Text without Supervision. Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao
2020	On a projective ensemble approach to two sample test for equality of distributions. Zhimei Li, Yaowu Zhang
2020	On hyperparameter tuning in general clustering problemsm. Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang
2020	On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes. Naoto Ohsaka, Tatsuya Matsuoka
2020	On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings. Mahmoud Assran, Mike Rabbat
2020	On the Expressivity of Neural Networks for Deep Reinforcement Learning. Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma
2020	On the Generalization Benefit of Noise in Stochastic Gradient Descent. Samuel L. Smith, Erich Elsen, Soham De
2020	On the Generalization Effects of Linear Transformations in Data Augmentation. Sen Wu, Hongyang R. Zhang, Gregory Valiant, Christopher Ré
2020	On the Global Convergence Rates of Softmax Policy Gradient Methods. Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans
2020	On the Global Optimality of Model-Agnostic Meta-Learning. Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
2020	On the Iteration Complexity of Hypergradient Computation. Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
2020	On the Noisy Gradient Descent that Generalizes as SGD. Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
2020	On the Number of Linear Regions of Convolutional Neural Networks. Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, Ling Shao
2020	On the Power of Compressed Sensing with Generative Models. Akshay Kamath, Eric Price, Sushrut Karmalkar
2020	On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation. Jianing Li, Yanyan Lan, Jiafeng Guo, Xueqi Cheng
2020	On the Sample Complexity of Adversarial Multi-Source PAC Learning. Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert
2020	On the Theoretical Properties of the Network Jackknife. Qiaohui Lin, Robert Lunde, Purnamrita Sarkar
2020	On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Sebastian Pokutta, Mohit Singh, Alfredo Torrico
2020	On the consistency of top-k surrogate losses. Forest Yang, Sanmi Koyejo
2020	One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. Wenlong Huang, Igor Mordatch, Deepak Pathak
2020	One Size Fits All: Can We Train One Denoiser for All Noise Levels? Abhiram Gnanasambandam, Stanley H. Chan
2020	One-shot Distributed Ridge Regression in High Dimensions. Yue Sheng, Edgar Dobriban
2020	Online Bayesian Moment Matching based SAT Solver Heuristics. Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh
2020	Online Continual Learning from Imbalanced Data. Aristotelis Chrysakis, Marie-Francine Moens
2020	Online Control of the False Coverage Rate and False Sign Rate. Asaf Weinstein, Aaditya Ramdas
2020	Online Convex Optimization in the Random Order Model. Dan Garber, Gal Korcia, Kfir Y. Levy
2020	Online Dense Subgraph Discovery via Blurred-Graph Feedback. Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
2020	Online Learned Continual Compression with Adaptive Quantization Modules. Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau
2020	Online Learning for Active Cache Synchronization. Andrey Kolobov, Sébastien Bubeck, Julian Zimmert
2020	Online Learning with Dependent Stochastic Feedback Graphs. Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
2020	Online Learning with Imperfect Hints. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
2020	Online Multi-Kernel Learning with Graph-Structured Feedback. Pouya M. Ghari, Yanning Shen
2020	Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Jinzhi Bu, David Simchi-Levi, Yunzong Xu
2020	Online metric algorithms with untrusted predictions. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon
2020	Online mirror descent and dual averaging: keeping pace in the dynamic case. Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander
2020	Operation-Aware Soft Channel Pruning using Differentiable Masks. Minsoo Kang, Bohyung Han
2020	Optimal Bounds between f-Divergences and Integral Probability Metrics. Rohit Agrawal, Thibaut Horel
2020	Optimal Continual Learning has Perfect Memory and is NP-hard. Jeremias Knoblauch, Hisham Husain, Tom Diethe
2020	Optimal Differential Privacy Composition for Exponential Mechanisms. Jinshuo Dong, David Durfee, Ryan Rogers
2020	Optimal Estimator for Unlabeled Linear Regression. Hang Zhang, Ping Li
2020	Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer. Alexey Drutsa
2020	Optimal Randomized First-Order Methods for Least-Squares Problems. Jonathan Lacotte, Mert Pilanci
2020	Optimal Robust Learning of Discrete Distributions from Batches. Ayush Jain, Alon Orlitsky
2020	Optimal Sequential Maximization: One Interview is Enough! Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati
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2020	Optimal transport mapping via input convex neural networks. Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason D. Lee
2020	Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing. Yuxuan Xie, Jilles Dibangoye, Olivier Buffet
2020	Optimistic Bounds for Multi-output Learning. Henry W. J. Reeve, Ata Kabán
2020	Optimistic Policy Optimization with Bandit Feedback. Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor
2020	Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. Yonatan Dukler, Quanquan Gu, Guido Montúfar
2020	Optimization and Analysis of the pAp@k Metric for Recommender Systems. Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain
2020	Optimization from Structured Samples for Coverage Functions. Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang
2020	Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret
2020	Optimizing Black-box Metrics with Adaptive Surrogates. Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya R. Gupta
2020	Optimizing Data Usage via Differentiable Rewards. Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime G. Carbonell, Graham Neubig
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2020	Oracle Efficient Private Non-Convex Optimization. Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
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2020	Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev
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2020	Predictive Coding for Locally-Linear Control. Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui
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2020	Predictive Sampling with Forecasting Autoregressive Models. Auke J. Wiggers, Emiel Hoogeboom
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2020	Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification. Hui Ye, Zhiyu Chen, Da-Han Wang, Brian D. Davison
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2020	Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event.
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2020	Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity. Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
2020	Projective Preferential Bayesian Optimization. Petrus Mikkola, Milica Todorovic, Jari Järvi, Patrick Rinke, Samuel Kaski
2020	Proper Network Interpretability Helps Adversarial Robustness in Classification. Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel
2020	Provable Representation Learning for Imitation Learning via Bi-level Optimization. Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi
2020	Provable Self-Play Algorithms for Competitive Reinforcement Learning. Yu Bai, Chi Jin
2020	Provable Smoothness Guarantees for Black-Box Variational Inference. Justin Domke
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2020	Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson
2020	Provably Efficient Exploration in Policy Optimization. Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
2020	Provably Efficient Model-based Policy Adaptation. Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao
2020	Proving the Lottery Ticket Hypothesis: Pruning is All You Need. Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
2020	Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup. Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
2020	Q-value Path Decomposition for Deep Multiagent Reinforcement Learning. Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, Zhongyu Wei
2020	Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. Runchao Ma, Qihang Lin, Tianbao Yang
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2020	RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr. Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou
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2020	Uncertainty-Aware Lookahead Factor Models for Quantitative Investing. Lakshay Chauhan, John Alberg, Zachary C. Lipton
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2020	Undirected Graphical Models as Approximate Posteriors. Arash Vahdat, Evgeny Andriyash, William G. Macready
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