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Google at NeurIPS 2020

December 7, 2020

Posted by Jaqui Herman and Cat Armato, Program Managers

This week marks the beginning of the 34th annual Conference on Neural Information Processing Systems (NeurIPS 2020), the biggest machine learning conference of the year. Held virtually for the first time, this conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. As a Platinum Sponsor of NeurIPS 2020, Google will have a strong presence with more than 180 accepted papers, additionally contributing to and learning from the broader academic research community via talks, posters, workshops and tutorials.

If you are registered for NeurIPS 2020, we hope you’ll visit our virtual booth and chat with our researchers about the projects and opportunities at Google that go into solving the world's most challenging research problems, and to see demonstrations of some of the exciting research we pursue, such as Transformers for image recognition, Tone Transfer, large-scale distributed RL, recreating historical streetscapes and much more. You can also learn more about our work being presented in the list below (Google affiliations highlighted in blue).

Organizing Committees

General Chair: Hugo Larochelle

Workshop Co-Chair: Sanmi Koyejo

Diversity and Inclusion Chairs include: Katherine Heller

Expo Chair: Pablo Samuel Castro

Senior Area Chairs include: Corinna Cortes, Fei Sha, Mohammad Ghavamzadeh, Sanjiv Kumar, Charles Sutton, Dale Schuurmans, David Duvenaud, Elad Hazan, Marco Cuturi, Peter Bartlett, Samy Bengio, Tong Zhang, Claudio Gentile, Kevin Murphy, Cordelia Schmid, Amir Globerson

Area Chairs include: Boqing Gong, Afshin Rostamizadeh, Alex Kulesza, Branislav Kveton, Craig Boutilier, Heinrich Jiang, Manzil Zaheer, Silvio Lattanzi, Slav Petrov, Srinadh Bhojanapalli, Rodolphe Jenatton, Mathieu Blondel, Aleksandra Faust, Alexey Dosovitskiy, Ashish Vaswani, Augustus Odena, Balaji Lakshminarayanan, Ben Poole, Colin Raffel, Danny Tarlow, David Ha, Denny Zhou, Dumitru Erhan, Dustin Tran, George Tucker, Honglak Lee, Ilya Tolstikhin, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Kevin Swersky, Matthew Johnson, Minmin Chen, Mohammad Norouzi, Moustapha Cisse, Naman Agarwal, Nicholas Carlini, Olivier Bachem, Tim Salimans, Vincent Dumoulin, Yann Dauphin, Andrew Dai, Izhak Shafran, Karthik Sridharan, Abhinav Gupta, Abhishek Kumar, Adam White, Aditya Menon, Kun Zhang, Ce Liu, Cristian Sminchisescu, Hossein Mobahi, Phillip IsolaTomer Koren, Chelsea Finn, Amin Karbasi, Mario Lučić

NeurIPS 2020 Foundation Board includes: Michael Mozer, Samy Bengio, Corinna Cortes, Hugo Larochelle, John C. Platt, Fernando Pereira

Accepted Papers

Rankmax: An Adaptive Projection Alternative to the Softmax Function
Weiwei Kong*, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang

Unsupervised Sound Separation Using Mixture Invariant Training
Scott Wisdom, Efthymios Tzinis*, Hakan Erdogan, Ron Weiss, Kevin Wilson, John Hershey

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai

Interpretable Sequence Learning for Covid-19 Forecasting
Sercan O. Arık, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister

Towards Learning Convolutions from Scratch
Behnam Neyshabur

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Minhae Kwon, Saurabh Daptardar, Paul Schrater, Xaq Pitkow

Off-Policy Evaluation via the Regularized Lagrangian
Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans

Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le

Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar

Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

MOReL: Model-Based Offline Reinforcement Learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao, Ting Liu, Xi Peng, Dimitris Metaxas

Generative View Synthesis: From Single-view Semantics to Novel-view Images
Tewodros Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker

PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang

Enabling Certification of Verification-Agnostic Networks via Memory-Efficient Semidefinite Programming
Sumanth Dathathri, Krishnamurthy (Dj) Dvijotham, Alex Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow*, Percy Liang, Pushmeet Kohli

An Analysis of SVD for Deep Rotation Estimation
Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Arun Ganesh*, Kunal Talwar*

DISK: Learning Local Features with Policy Gradient
Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls

Robust Large-margin Learning in Hyperbolic Space
Melanie Weber*, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner, Igor Mordatch, Sergey Levine

Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Faster DBSCAN via Subsampled Similarity Queries
Heinrich Jiang, Jennifer Jang, Jakub Łacki

Exact Recovery of Mangled Clusters with Same-Cluster Queries
Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Görür, Chris Harris, Dale Schuurmans

Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

Efficient Active Learning of Sparse Halfspaces with Arbitrary Bounded Noise
Chicheng Zhang, Jie Shen, Pranjal Awasthi

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Synthetic Data Generators -- Sequential and Private
Olivier Bousquet, Roi Livni, Shay Moran

Learning Discrete Distributions: User vs Item-level Privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Michael Riley

Learning Differential Equations that are Easy to Solve
Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David K. Duvenaud

An Optimal Elimination Algorithm for Learning a Best Arm
Avinatan Hassidim, Ron Kupfer, Yaron Singer

The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, Krunal Kishor Patel*, Juan Pablo Vielma

Escaping the Gravitational Pull of Softmax
Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li*, Csaba Szepesvari, Dale Schuurmans

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

PAC-Bayes Learning Bounds for Sample-Dependent Priors
Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
Sarah Perrin, Julien Perolat, Mathieu Lauriere, Matthieu Geist, Romuald Elie, Olivier Pietquin

What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert Baldock*, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

Online Planning with Lookahead Policies
Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor

Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

Differentially Private Clustering: Tight Approximation Ratios
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
Aranyak Mehta, Uri Nadav, Alexandros Psomas*, Aviad Rubinstein

Myersonian Regression
Allen Liu, Renato Leme, Jon Schneider

Assisted Learning: A Framework for Multi-Organization Learning
Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

Adversarial Robustness via Robust Low Rank Representations
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan

Multi-Plane Program Induction with 3D Box Priors
Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu

Privacy Amplification via Random Check-Ins
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta

Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

Online Agnostic Boosting via Regret Minimization
Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran*

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

Faithful Embeddings for Knowledge Base Queries
Haitian Sun, Andrew Arnold*, Tania Bedrax Weiss, Fernando Pereira, William W. Cohen

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

An Operator View of Policy Gradient Methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux

Reinforcement Learning with Feedback Graphs
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Adam Smith, Shuang Song, Abhradeep Thakurta

What is Being Transferred in Transfer Learning?
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier

MetaSDF: Meta-Learning Signed Distance Functions
Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein

Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

Robust Optimization for Fairness with Noisy Protected Groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael I. Jordan

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans

Breaking the Communication-Privacy-Accuracy Trilemma
Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

Differentiable Meta-Learning of Bandit Policies
Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer

Multi-Stage Influence Function
Hongge Chen*, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh

Compositional Visual Generation with Energy Based Models
Yilun Du, Shuang Li, Igor Mordatch

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

Curriculum By Smoothing
Samarth Sinha, Animesh Garg, Hugo Larochelle

Online Linear Optimization with Many Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Prediction with Corrupted Expert Advice
Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour

Agnostic Learning with Multiple Objectives
Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus

CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers*, Andrea Tagliasacchi, Otmar Hilliges

Reparameterizing Mirror Descent as Gradient Descent
Ehsan Amid, Manfred K. Warmuth

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition
Ben Adlam, Jeffrey Pennington

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Zhe Dong, Andriy Mnih, George Tucker

Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton

JAX MD: A Framework for Differentiable Physics
Samuel S. Schoenholz, Ekin D. Cubuk

Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

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

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo P. Monti, Diederik P. Kingma, Aapo Hyvärinen

Demystifying Orthogonal Monte Carlo and Beyond
Han Lin, Haoxian Chen, Tianyi Zhang, Clement Laroche, Krzysztof Choromanski

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel

Compositional Generalization via Neural-Symbolic Stack Machines
Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou

Universally Quantized Neural Compression
Eirikur Agustsson, Lucas Theis

Self-Distillation Amplifies Regularization in Hilbert Space
Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett

ShapeFlow: Learnable Deformation Flows Among 3D Shapes
Chiyu “Max" Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi

High-Fidelity Generative Image Compression
Fabian Mentzer*, George Toderici, Michael Tschannen*, Eirikur Agustsson

COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu, Li K. Wenliang, Michael Munn, Beatrice Acciaio

When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
Victor Veitch, Anisha Zaveri

Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Sajad Norouzi, David J. Fleet, Mohamamd Norouzi

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
Consistent Plug-in Classifiers for Complex Objectives and Constraints
Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan

Online MAP Inference of Determinantal Point Processes
Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam

Organizing Recurrent Network Dynamics by Task-computation to Enable Continual Learning
Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Neural Execution Engines: Learning to Execute Subroutines
Yujun Yan*, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Spin-Weighted Spherical CNNs
Carlos Esteves, Ameesh Makadia, Kostas Daniilidis

An Efficient Nonconvex Reformulation of Stagewise Convex Optimization Problems
Rudy R. Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham

Stochastic Optimization with Laggard Data Pipelines
Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar*, Cyril Zhang*

Regularizing Towards Permutation Invariance In Recurrent Models
Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

Fast and Accurate kk-means++ via Rejection Sampling
Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler*, Ola Svensson

Fairness Without Demographics Through Adversarially Reweighted Learning
Preethi Lahoti*, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi

Gradient Estimation with Stochastic Softmax Tricks
Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

A Spectral Energy Distance for Parallel Speech Synthesis
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

Ode to an ODE
Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov, Xingyou Song, Jean-Jacques Slotine, Jacob Varley, Honglak Lee, Adrian Weller, Vikas Sindhwani

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc Le

On Adaptive Attacks to Adversarial Example Defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry

Fair Performance Metric Elicitation
Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi O. Koyejo

Robust Pre-Training by Adversarial Contrastive Learning
Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Why are Adaptive Methods Good for Attention Models?
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le

Fair Hierarchical Clustering
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

Fairness with Overlapping Groups; a Probabilistic Perspective
Forest Yang*, Moustapha Cisse, Sanmi Koyejo

Differentiable Top-k with Optimal Transport
Yujia Xie*, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Katherine Hermann, Ting Chen, Simon Kornblith

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

Evaluating Attribution for Graph Neural Networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy Colwell, Alexander Wiltschko

Sliding Window Algorithms for k-Clustering Problems
Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran*, Alex Irpan, Eric Jang

What Makes for Good Views for Contrastive Learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

Supervised Contrastive Learning
Prannay Khosla*, Piotr Teterwak*, Chen Wang*, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Off-Policy Imitation Learning from Observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

Effective Diversity in Population Based Reinforcement Learning
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee

Object-Centric Learning with Slot Attention
Francesco Locatello*, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

On the Power of Louvain in the Stochastic Block Model
Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

SMYRF - Efficient Attention using Asymmetric Clustering
Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis

Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

WOR and p's: Sketches for ℓp-Sampling Without Replacement
Edith Cohen, Rasmus Pagh, David P. Woodruff

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, Ren Ng

Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

Adapting to Misspecification in Contextual Bandits
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Nino Vieillard, Tadashi Kozunoú, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

Learning with Differentiable Pertubed Optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Munchausen Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

Sample Complexity of Uniform Convergence for Multicalibration
Eliran Shabat, Lee Cohen, Yishay Mansour

Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

Most ReLU Networks Suffer from ℓ² Adversarial Perturbations
Amit Daniely, Hadas Shacham

Geometric Exploration for Online Control
Orestis Plevrakis, Elad Hazan

PLLay: Efficient Topological Layer Based on Persistent Landscapes
Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frederic Chazal, Larry Wasserman

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu*, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan

Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Conic Descent and its Application to Memory-efficient Optimization Over Positive Semidefinite Matrices
John Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye

On the Training Dynamics of Deep Networks with L₂ Regularization
Aitor Lewkowycz, Guy Gur-Ari

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu*, Lechao Xiao, Ben Adlam, Jeffrey Pennington

Adaptive Probing Policies for Shortest Path Routing
Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala

Optimal Approximation — Smoothness Tradeoffs for Soft-Max Functions
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen

Learning Graph Structure With A Finite-State Automaton Layer
Daniel Johnson, Hugo Larochelle, Daniel Tarlow

Estimating Training Data Influence by Tracing Gradient Descent
Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan

Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu, Yijie Guo, Honglak Lee, John Canny, Sergio Guadarrama


Designing Learning Dynamics
Organizers: Marta Garnelo, David Balduzzi, Wojciech Czarnecki

Where Neuroscience meets AI (And What’s in Store for the Future)
Organizers: Jane Wang, Kevin Miller, Adam Marblestone

Offline Reinforcement Learning: From Algorithm Design to Practical Applications
Organizers: Sergey Levine, Aviral Kumar

Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning
Organizers: Dustin Tran, Balaji Lakshminarayanan, Jasper Snoek

Abstraction & Reasoning in AI systems: Modern Perspectives
Organizers: Francois Chollet, Melanie Mitchell, Christian Szegedy

Policy Optimization in Reinforcement Learning
Organizers: Sham M Kakade, Martha White, Nicolas Le Roux

Federated Learning and Analytics: Industry Meets Academia
Organizers: Brendan McMahan, Virginia Smith, Peter Kairouz

Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization
Organizers: David Duvenaud, J. Zico Kolter, Matthew Johnson

Beyond Accuracy: Grounding Evaluation Metrics for Human-Machine Learning Systems
Organizers: Praveen Chandar, Fernando Diaz, Brian St. Thomas


Black in AI Workshop @ NeurIPS 2020 (Diamond Sponsor)
Mentorship Roundtables: Natasha Jacques

LatinX in AI Workshop @ NeurIPS 2020 (Platinum Sponsor)
Organizers include: Pablo Samuel Castro
Invited Speaker: Fernanda Viégas
Mentorship Roundtables: Tomas Izo

Queer in AI Workshop @ NeurIPS 2020 (Platinum Sponsor)
Organizers include: Raphael Gontijo Lopes

Women in Machine Learning (Platinum Sponsor)
Organizers include: Xinyi Chen, Jessica Schrouff
Invited Speaker: Fernanda Viégas
Sponsor Talk: Jessica Schrouff
Mentorship Roundtables: Hanie Sedghi, Marc Bellemare, Katherine Heller, Rianne van den Berg, Natalie Schluter, Colin Raffel, Azalia Mirhoseini, Emily Denton, Jesse Engel, Anusha Ramesh, Matt Johnson, Jeff Dean, Laurent Dinh, Samy Bengio, Yasaman Bahri, Corinna Cortes, Nicolas le Roux, Hugo Larochelle, Sergio Guadarrama, Natasha Jaques, Pablo Samuel Castro, Elaine Le, Cory Silvear

Muslims in ML
Organizers include: Mohammad Norouzi

Resistance AI Workshop
Organizers include: Elliot Creager, Raphael Gontijo Lopes

Privacy Preserving Machine Learning — PriML and PPML Joint Edition
Organizers include: Adria Gascon, Mariana Raykova

OPT2020: Optimization for Machine Learning
Organizers include: Courtney Paquette

Machine Learning for Health (ML4H): Advancing Healthcare for All
Organizers include: Subhrajit Roy

Human in the Loop Dialogue Systems
Organizers include: Rahul Goel
Invited Speaker: Ankur Parikh

Self-Supervised Learning for Speech and Audio Processing
Organizers include: Tara Sainath
Invited Speaker: Bhuvana Ramabhadran

3rd Robot Learning Workshop
Organizers include: Alex Bewley, Vincent Vanhoucke
Invited Speaker: Pete Florence

Workshop on Deep Learning and Inverse Problems
Invited Speaker: Peyman Milanfar

Crowd Science Workshop: Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation
Invited Speakers: Lora Aroyo, Praveen Paritosh

Workshop on Fair AI in Finance
Invited Speakers: Berk Ustun, Madeleine Clare Elish

Object Representations for Learning and Reasoning
Panel Moderator: Klaus Greff

Deep Reinforcement Learning
Organizers include: Chelsea Finn
Invited Speaker: Marc Bellemare

Algorithmic Fairness Through the Lens of Causality and Interpretability
Organizers include: Awa Dieng, Jessica Schrouff, Fernando Diaz

Machine Learning for the Developing World (ML4D)
Steering Committee Member: Ernest Mwebaze

Machine Learning for Engineering Modeling, Simulation and Design
Organizers include: Stephan Hoyer

Machine Learning for Creativity and Design
Organizers include: Adam Roberts, Daphne Ippolito
Invited Speaker: Jesse Engel

Cooperative AI
Invited Speaker: Natasha Jaques

International Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL 2020)
Invited Speaker: Brendan McMahan

Machine Learning for Molecules
Organizers include: Jennifer Wei
Invited Speaker: Benjamin Sanchez-Lengeling

Navigating the Broader Impacts of AI Research
Panelists include: Nyalleng Moorosi, Colin Raffel, Natalie Schluter, Ben Zevenbergen

Beyond BackPropagation: Novel Ideas for Training Neural Architectures
Organizers include: Yanping Huang

Differentiable Computer Vision, Graphics, and Physics in Machine Learning
Invited Speaker: Andrea Tagliasacchi

AI for Earth Sciences
Invited Speaker: Milind Tambe

Machine Learning for Mobile Health
Organizers include: Katherine Heller, Marianne Njifon

Shared Visual Representations in Human and Machine Intelligence (SVRHM)
Invited Speaker: Gamaleldin Elsayed

The Challenges of Real World Reinforcement Learning
Organizers include: Gabriel Dulac-Arnold
Invited Speaker: Chelsea Finn

Workshop on Computer Assisted Programming (CAP)
Organizers include: Charles Sutton, Augustus Odena

Self-Supervised Learning — Theory and Practice
Organizers include: Barret Zoph
Invited Speaker: Quoc V. Le

Offline Reinforcement Learning
Organizers include: Rishabh Agarwal, George Tucker

Machine Learning for Systems
Organizers include: Anna Goldie, Azalia Mirhoseini, Martin Maas
Invited Speaker: Ed Chi

Deep Learning Through Information Geometry
Organizers include: Alexander Alemi


Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning
Organizers include: Sal Candido

Accelerating Eye Movement Research via Smartphone Gaze
Organizers include: Vidhya Navalpakkam

Mining and Learning with Graphs at Scale
Organizers include: Bryan Perozzi, Vahab Mirrokni, Jonathan Halcrow, Jakub Lacki

*Work performed while at Google