NeurIPS 2020 : Featured

Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, Michal Valko

Can Temporal-Diﬀerence and Q-Learning Learn Representation? A Mean-Field Theory

Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang

Causal Imitation Learning With Unobserved Confounders

Junzhe Zhang, Daniel Kumor, Elias Bareinboim

Causal Intervention for Weakly-Supervised Semantic Segmentation

Dong Zhang, hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Compositional Explanations of Neurons

Jesse Mu, Jacob Andreas

Continual Deep Learning by Functional Regularisation of Memorable Past

Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Emtiyaz Khan

Contrastive learning of global and local features for medical image segmentation with limited annotations

Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu

Convex optimization based on global lower second-order models

Nikita Doikov, Yurii Nesterov

Convolutional Generation of Textured 3D Meshes

Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators

Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Helen Li

Deep Energy-based Modeling of Discrete-Time Physics

Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi

Deep Transformation-Invariant Clustering

Tom Monnier, Thibault Groueix, Mathieu Aubry

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

Jiangxin Dong, Stefan Roth, Bernt Schiele

Differentially Private Clustering: Tight Approximation Ratios

Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Dissecting Neural ODEs

Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama

Do Adversarially Robust ImageNet Models Transfer Better?

Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization

Jonathan Lacotte, Mert Pilanci

Efficient active learning of sparse halfspaces with arbitrary bounded noise

Chicheng Zhang, Jie Shen, Pranjal Awasthi

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi

Equivariant Networks for Hierarchical Structures

Ren Wang, Marjan Albooyeh, Siamak Ravanbakhsh

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Nikolaos Karalias, Andreas Loukas

Escaping the Gravitational Pull of Softmax

Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvari, Dale Schuurmans

Exact Recovery of Mangled Clusters with Same-Cluster Queries

Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun

Fair regression via plug-in estimator and recalibration with statistical guarantees

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

Fast and Flexible Temporal Point Processes with Triangular Maps

Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann

FrugalML: How to use ML Prediction APIs more accurately and cheaply

Lingjiao Chen, Matei Zaharia, James Zou

Fully Dynamic Algorithm for Constrained Submodular Optimization

Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub Tarnawski, Morteza Zadimoghaddam

Gibbs Sampling with People

Peter Harrison, Raja Marjieh, Fede G Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon

Gradient Estimation with Stochastic Softmax Tricks

Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Graph Cross Networks with Vertex Infomax Pooling

Maosen Li, Siheng Chen, Ya Zhang, Ivor Tsang

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang

Greedy inference with structure-exploiting lazy maps

Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef Marzouk

Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems

Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer

High-Fidelity Generative Image Compression

Fabian Mentzer, George D Toderici, Michael Tschannen, Eirikur Agustsson

Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

Benjamin Recht, Chris Ré, Stephen Wright, Feng Niu

Implicit Neural Representations with Periodic Activation Functions

Vincent Sitzmann, Julien N.P Martel, Alexander Bergman, David Lindell, Gordon Wetzstein

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows

Cheng Zhang

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method

Michal Derezinski, Rajiv Khanna, Michael W Mahoney

Is normalization indispensable for training deep neural network?

Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj

Kernel Methods Through the Roof: Handling Billions of Points Efficiently

Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi

Language Models are Few-Shot Learners

Tom B Brown, Ben Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen M Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

Learning Composable Energy Surrogates for PDE Order Reduction

Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan Adams

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence

Feng Liu, Xiaoming Liu

Learning Individually Inferred Communication for Multi-Agent Cooperation

Ziluo Ding, Tiejun Huang, Zongqing Lu

Learning Parities with Neural Networks

Amit Daniely, Eran Malach

Learning Physical Graph Representations from Visual Scenes

Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel Yamins

Learning abstract structure for drawing by efficient motor program induction

Lucas Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum

Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces

Akash Saha, Balamurugan Palaniappan

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Remi Munos, Matthieu Geist

Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms

Tom Berrett, Cristina Butucea

Look-ahead Meta Learning for Continual Learning

Gunshi Gupta, Karmesh Yadav, Liam Paull

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

Metric-Free Individual Fairness in Online Learning

Yahav Bechavod, Christopher Jung, Steven Wu

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff

Multi-label Contrastive Predictive Coding

Jiaming Song, Stefano Ermon

Multiscale Deep Equilibrium Models

Shaojie Bai, Vladlen Koltun, J. Zico Kolter

Network-to-Network Translation with Conditional Invertible Neural Networks

Robin Rombach, Patrick Esser, Bjorn Ommer

NeuMiss networks: differentiable programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gael Varoquaux

Neural encoding with visual attention

Meenakshi Khosla, Gia Ngo, Keith Jamison, Amy Kuceyeski, Mert Sabuncu

No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium

Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data

Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin

Novelty Search in Representational Space for Sample Efficient Exploration

David Tao, Vincent Francois-Lavet, Joelle Pineau

On the Modularity of Hypernetworks

Tomer Galanti, Lior Wolf

On the training dynamics of deep networks with $L_2$ regularization

Aitor Lewkowycz, Guy Gur-Ari

Online Sinkhorn: Optimal Transport distances from sample streams

Arthur Mensch, Gabriel Peyré

Partially View-aligned Clustering

Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander Hauptmann

Point process models for sequence detection in high-dimensional neural spike trains

Alex Williams, Anthony Degleris, Yixin Wang, Scott Linderman

PyGlove: Symbolic Programming for Automated Machine Learning

Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc V Le

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

Tao Fang, Yu Qi, Gang Pan

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan, Florent Krzakala

Rethinking Pre-training and Self-training

Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Dogus Cubuk, Quoc V Le

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

Benjamin Eysenbach, XINYANG GENG, Sergey Levine, Russ Salakhutdinov

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs

Edouard Leurent, Odalric-Ambrym Maillard, Denis Efimov

SLIP: Learning to predict in unknown dynamical systems with long-term memory

Paria Rashidinejad, Jiantao Jiao, Stuart Russell

Self-Paced Deep Reinforcement Learning

Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Space-Time Correspondence as a Contrastive Random Walk

Allan Jabri, Andrew Owens, Alexei Efros

Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks

Zhou Fan, Zhichao Wang

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

The Cone of Silence: Speech Separation by Localization

Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

Katherine L. Hermann, Ting Chen, Simon Kornblith

The Primal-Dual method for Learning Augmented Algorithms

Etienne Bamas, Andreas Maggiori, Ola Svensson

The interplay between randomness and structure during learning in RNNs

Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, Omri Barak

Theory-Inspired Path-Regularized Differential Network Architecture Search

Pan Zhou, Caiming Xiong, Richard Socher, Steven Hoi

Towards a Better Global Loss Landscape of GANs

Ruoyu Sun, Tiantian Fang, Alex Schwing

Training Generative Adversarial Networks with Limited Data

Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe

Transferable Graph Optimizers for ML Compilers

yanqiz Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Phothilimtha, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon

Ultra-Low Precision 4-bit Training of Deep Neural Networks

Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks

Ryo Karakida, Kazuki Osawa

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

Zhaozhi Qian, Ahmed Alaa, Mihaela van der Schaar

Worst-Case Analysis for Randomly Collected Data

Justin Chen, Gregory Valiant, Paul Valiant