NeurIPS 2020

1,898 papers

Fast Unbalanced Optimal Transport on a Tree

Ryoma Sato, Makoto Yamada, Hisashi Kashima

Provable Overlapping Community Detection in Weighted Graphs

Jimit Majmudar, Stephen Vavasis

Time-Reversal Symmetric ODE Network

In Huh, Eunho Yang, Sung Ju Hwang, Jinwoo Shin

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

Katherine Hermann, Ting Chen, Simon Kornblith

Hedging in games: Faster convergence of external and swap regrets

Xi Chen, Binghui Peng

ColdGANs: Taming Language GANs with Cautious Sampling Strategies

Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

Aaron Sonabend, Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits

Experimental design for MRI by greedy policy search

Tim Bakker, Herke van Hoof, Max Welling

Almost Surely Stable Deep Dynamics

Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni

Content Provider Dynamics and Coordination in Recommendation Ecosystems

Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks

Kenta Oono, Taiji Suzuki

Tensor Completion Made Practical

Allen Liu, Ankur Moitra

Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev

Xiao Wang, Qi Lei, Ioannis Panageas

Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity

Dan Garber

R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making

Sergey Shuvaev, Sarah Starosta, Duda Kvitsiani, Adam Kepecs, Alexei Koulakov

Task-Robust Model-Agnostic Meta-Learning

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers

Heng Yang, Luca Carlone

Modern Hopfield Networks and Attention for Immune Repertoire Classification

Michael Widrich, Bernhard Schäfl, Milena Pavlović, Hubert Ramsauer, Lukas Gruber

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding

Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

Interpretable Sequence Learning for Covid-19 Forecasting

Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek

Preference-based Reinforcement Learning with Finite-Time Guarantees

Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, Artur Dubrawski

Deep Metric Learning with Spherical Embedding

Dingyi Zhang, Yingming Li, Zhongfei Zhang

Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows

Cheng Zhang

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection

Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo

Collegial Ensembles

Etai Littwin, Ben Myara, Sima Sabah, Joshua Susskind, Shuangfei Zhai

Neural Non-Rigid Tracking

Aljaz Bozic, Pablo Palafox, Michael Zollhöfer, Angela Dai, Justus Thies

Weak Form Generalized Hamiltonian Learning

Kevin Course, Trefor Evans, Prasanth Nair

Learning Agent Representations for Ice Hockey

Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan

Meta-trained agents implement Bayes-optimal agents

Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic

Learning Optimal Representations with the Decodable Information Bottleneck

Yann Dubois, Douwe Kiela, David J Schwab, Ramakrishna Vedantam

Supervised Contrastive Learning

Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Pantelis Elinas, Edwin V. Bonilla, Louis Tiao

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables

Zhe Dong, Andriy Mnih, George Tucker

Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model

Yiwei Shen, Pierre C Bellec

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space

Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc V Le, Quoc V. Le

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference

Disi Ji, Padhraic Smyth, Mark Steyvers

Measuring Robustness to Natural Distribution Shifts in Image Classification

Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht

OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling

Viet Huynh, He Zhao, Dinh Phung

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Aviral Kumar, Abhishek Gupta, Sergey Levine

A Tight Lower Bound and Efficient Reduction for Swap Regret

Shinji Ito

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise

Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks

Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney

All your loss are belong to Bayes

Christian Walder, Richard Nock

Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks

Zixuan Ke, Bing Liu, Xingchang Huang

GCN meets GPU: Decoupling “When to Sample” from “How to Sample”

Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut Kandemir

Pre-training via Paraphrasing

Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang

How do fair decisions fare in long-term qualification?

Xueru Zhang, Ruibo Tu, Yang Liu, mingyan liu, Hedvig Kjellström

Ensuring Fairness Beyond the Training Data

Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel J. Hsu

CoMIR: Contrastive Multimodal Image Representation for Registration

Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby

A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices

Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang

What if Neural Networks had SVDs?

Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin

Choice Bandits

Arpit Agarwal, Nicholas Johnson, Shivani Agarwal

Batch normalization provably avoids ranks collapse for randomly initialised deep networks

Hadi Daneshmand, Jonas Köhler, Francis R. Bach, Thomas Hofmann, Aurelien Lucchi

What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes

Herman Yau, Chris Russell, Simon Hadfield

Regularizing Towards Permutation Invariance In Recurrent Models

Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization

Dmitry Kovalev, Adil SALIM, Peter Richtarik

BERT Loses Patience: Fast and Robust Inference with Early Exit

Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu

Making Non-Stochastic Control (Almost) as Easy as Stochastic

Max Simchowitz

Decentralized Accelerated Proximal Gradient Descent

Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang

X-CAL: Explicit Calibration for Survival Analysis

Mark Goldstein, Xintian Han, Aahlad Puli, Adler J. Perotte, Rajesh Ranganath

The MAGICAL Benchmark for Robust Imitation

Sam Toyer, Rohin Shah, Andrew Critch, Stuart J. Russell

Precise expressions for random projections: Low-rank approximation and randomized Newton

Michal Derezinski, Feynman T Liang, Zhenyu Liao, Michael W. Mahoney

An Improved Analysis of Stochastic Gradient Descent with Momentum

Yanli Liu, Yuan Gao, Wotao Yin

The Convolution Exponential and Generalized Sylvester Flows

Emiel Hoogeboom, Victor Garcia Satorras, Jakub Tomczak, Max Welling

Improving model calibration with accuracy versus uncertainty optimization

Ranganath Krishnan, Omesh Tickoo

Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting

Ziping Xu, Ambuj Tewari

Byzantine Resilient Distributed Multi-Task Learning

Jiani Li, Waseem Abbas, Xenofon Koutsoukos

Learning Robust Decision Policies from Observational Data

Muhammad Osama, Dave Zachariah, Peter Stoica

Truthful Data Acquisition via Peer Prediction

Yiling Chen, Yiheng Shen, Shuran Zheng

Worst-Case Analysis for Randomly Collected Data

Justin Chen, Gregory Valiant, Paul Valiant

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods

Laurence Aitchison

3D Self-Supervised Methods for Medical Imaging

Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner

Learning to Prove Theorems by Learning to Generate Theorems

Mingzhe Wang, Jia Deng

Decision trees as partitioning machines to characterize their generalization properties

Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding

Zhu Zhang, Zhou Zhao, Zhijie Lin, jieming zhu, Xiuqiang He

Bi-level Score Matching for Learning Energy-based Latent Variable Models

Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu

WoodFisher: Efficient Second-Order Approximation for Neural Network Compression

Sidak Pal Singh, Dan Alistarh

Hybrid Models for Learning to Branch

Prateek Gupta, Maxime Gasse, Elias Khalil, Pawan Mudigonda, Andrea Lodi

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Guy Lorberbom, Chris J Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data

Utkarsh Ojha, Krishna Kumar Singh, Cho-jui Hsieh, Yong Jae Lee

Effective Diversity in Population Based Reinforcement Learning

Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, Stephen J. Roberts

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner

Distributed Newton Can Communicate Less and Resist Byzantine Workers

Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling

Moshe Eliasof, Eran Treister

Inverse Learning of Symmetries

Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp

Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment

Yuan Chen, Donglin Zeng, Tianchen Xu, Yuanjia Wang

Fair Multiple Decision Making Through Soft Interventions

Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

Houwen Peng, Hao Du, Hongyuan Yu, Qi Li, Jing Liao

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Stochastic Stein Discrepancies

Jackson Gorham, Anant Raj, Lester W. Mackey

PAC-Bayesian Bound for the Conditional Value at Risk

Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson

Digraph Inception Convolutional Networks

Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum

Neural Anisotropy Directions

Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, Pascal Frossard

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Micah Goldblum, Liam Fowl, Tom Goldstein

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets

Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

Robust large-margin learning in hyperbolic space

Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya K Menon, Sanjiv Kumar