Google at ICML 2021

Posted by Cat Armato and Jaqui Herman, Program Managers

Groups across Google are actively pursuing research across the field of machine learning, ranging from theory to application. With scalable tools and architectures, we build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, and more.

Google is proud to be a Platinum Sponsor of the thirty-eighth International Conference on Machine Learning (ICML 2021), a premier annual event happening this week. As a leader in machine learning research — with over 100 accepted publications and Googlers participating in workshops — we look forward to our continued partnership with the broader machine learning research community.

Registered for ICML 2021? We hope you’ll visit the Google virtual booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Take a look below to learn more about the Google research being presented at ICML 2021 (Google affiliations in bold).

Organizing Committee
ICML Board Members include: Corinna Cortes, Hugo Larochelle, Shakir Mohamed
ICML Emeritus Board includes: William Cohen, Andrew McCallum
Tutorial Co-Chair member: Quoc Lee

Attention Is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas

Scalable Evaluation of Multi-agent Reinforcement Learning with Melting Pot
Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel

On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li*, Csaba Szepesvari, Dale Schuurmans

Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

PID Accelerated Value Iteration Algorithm
Amir-Massoud Farahmand, Mohammad Ghavamzadeh

Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour

What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson

Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist

Revisiting Rainbow: Promoting More Insightful and Inclusive Deep Reinforcement Learning Research (see blog post)
Johan S. Obando-Ceron, Pablo Samuel Castro

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Seyed Kamyar Seyed Ghasemipour*, Dale Schuurmans, Shixiang Shane Gu

Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer

Tilting the Playing Field: Dynamical Loss Functions for Machine Learning
Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu

Model-Based Reinforcement Learning via Latent-Space Collocation
Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine

Momentum Residual Neural Networks
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

OmniNet: Omnidirectional Representations from Transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler

Synthesizer: Rethinking Self-Attention for Transformer Models
Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng

Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le

Randomized Entity-wise Factorization for Multi-agent Reinforcement Learning
Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha

LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy

Emergent Social Learning via Multi-agent Reinforcement Learning
Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques

Improved Contrastive Divergence Training of Energy-Based Models
Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch

Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Ziheng Jiang*, Chiyuan Zhang, Kunal Talwar, Michael Mozer

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar

EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc V. Le

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Paul Vicol, Luke Metz, Jascha Sohl-Dickstein

Federated Composite Optimization
Honglin Yuan*, Manzil Zaheer, Sashank Reddi

Light RUMs
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

Catformer: Designing Stable Transformers via Sensitivity Analysis
Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang

Representation Matters: Offline Pretraining for Sequential Decision Making
Mengjiao Yang, Ofir Nachum

Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Jongwook Choi*, Archit Sharma*, Honglak Lee, Sergey Levine, Shixiang Shane Gu

Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux

Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Piotr Teterwak*, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer

Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu

Hyperparameter Selection for Imitation Learning
Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin

Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo

Debiasing a First-Order Heuristic for Approximate Bi-Level Optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller

Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans

Composing Normalizing Flows for Inverse Problems
Jay Whang, Erik Lindgren, Alexandros Dimakis

Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Asaf Cassel, Tomer Koren

Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah

Fairness and Bias in Online Selection
Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard

The Impact of Record Linkage on Learning from Feature Partitioned Data
Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne

Reserve Price Optimization for First Price Auctions in Display Advertising
Zhe Feng*, Sébastien Lahaie, Jon Schneider, Jinchao Ye

A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh

A Statistical Perspective on Distillation
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar

Best Model Identification: A Rested Bandit Formulation
Leonardo Cella, Massimiliano Pontil, Claudio Gentile

Generalised Lipschitz Regularisation Equals Distributional Robustness
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith

Stochastic Multi-armed Bandits with Unrestricted Delay Distributions
Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour

Regularized Online Allocation Problems: Fairness and Beyond
Santiago Balseiro, Haihao Lu, Vahab Mirrokni

Implicit Rate-Constrained Optimization of Non-decomposable Objectives
Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter

Leveraging Non-uniformity in First-Order Non-Convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans

Dynamic Balancing for Model Selection in Bandits and RL
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit

Adversarial Dueling Bandits
Aadirupa Saha, Tomer Koren, Yishay Mansour

Optimizing Black-Box Metrics with Iterative Example Weighting
Gaurush Hiranandani*, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Oluwasanmi Koyejo

Relative Deviation Margin Bounds
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh

MC-LSTM: Mass-Conserving LSTM
Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer

12-Lead ECG Reconstruction via Koopman Operators
Authors:Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha

Finding Relevant Information via a Discrete Fourier Expansion
Mohsen Heidari, Jithin Sreedharan, Gil Shamir, Wojciech Szpankowski

LEGO: Latent Execution-Guided Reasoning for Multi-hop Question Answering on Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou

SpreadsheetCoder: Formula Prediction from Semi-structured Context
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

Combinatorial Blocking Bandits with Stochastic Delays
Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramani, Sanjay Shakkottai

Beyond log2(T) Regret for Decentralized Bandits in Matching Markets
Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman

Robust Pure Exploration in Linear Bandits with Limited Budget
Ayya Alieva, Ashok Cutkosky, Abhimanyu Das

Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (see blog post)
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig

On Linear Identifiability of Learned Representations
Geoffrey Roeder, Luke Metz, Diederik P. Kingma

Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile

Differentially Private Quantiles
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

Active Covering
Heinrich Jiang, Afshin Rostamizadeh

Sharf: Shape-Conditioned Radiance Fields from a Single View
Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

Learning a Universal Template for Few-Shot Dataset Generalization
Eleni Triantafillou*, Hugo Larochelle, Richard Zemel, Vincent Dumoulin

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot

Neural Feature Matching in Implicit 3D Representations
Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves

Locally Private k-Means in One Round
Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Large-Scale Meta-learning with Continual Trajectory Shifting
Jaewoong Shin, Hae Beom Lee, Boqing Gong, Sung Ju Hwang

Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis

Oneshot Differentially Private Top-k Selection
Gang Qiao, Weijie J. Su, Li Zhang

Unsupervised Part Representation by Flow Capsules
Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet

Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar

Practical and Private (Deep) Learning Without Sampling or Shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha

Leveraging Public Data for Practical Private Query Release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Zhiwei Steven Wu

Meta-Thompson Sampling
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári

Implicit-PDF: Non-parametric Representation of Probability Distributions on the Rotation Manifold
Kieran A Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

Improving Ultrametrics Embeddings Through Coresets
Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde

A Discriminative Technique for Multiple-Source Adaptation
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang

Self-Supervised and Supervised Joint Training for Resource-Rich Machine Translation
Yong Cheng, Wei Wang*, Lu Jiang, Wolfgang Macherey

Correlation Clustering in Constant Many Parallel Rounds
Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski

Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi

Meta-learning Bidirectional Update Rules
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Aguera y Arcas

Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin, David G.T. Barrett

Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
Haitian Sun*, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen

Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Samuel Schoenholz, Ekin Cubuk

Locally Adaptive Label Smoothing Improves Predictive Churn
Dara Bahri, Heinrich Jiang

Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
Patrick H. Chen, Wei Wei, Cho-jui Hsieh, Bo Dai

Workshops (only Google affiliations are noted)
LatinX in AI (LXAI) Research at ICML 2021
Hosts: Been Kim, Natasha Jaques

Uncertainty and Robustness in Deep Learning
Organizers: Balaji Lakshminarayanan, Jasper Snoek Invited Speaker: Dustin Tran

Reinforcement Learning for Real Life
Organizers: Minmin Chen, Lihong Li Invited Speaker: Ed Chi

Interpretable Machine Learning in Healthcare
Organizers: Alan Karthikesalingam Invited Speakers: Abhijit Guha Roy, Jim Winkens

The Neglected Assumptions in Causal Inference
Organizer: Alexander D’Amour

ICML Workshop on Algorithmic Recourse
Invited Speakers: Been Kim, Berk Ustun

A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning
Invited Speaker: Nicholas Carlini

Overparameterization: Pitfalls and Opportunities
Organizers: Yasaman Bahri, Hanie Sedghi

Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3)
Invited Speaker: Thomas Steinke

Beyond First-Order Methods in Machine Learning Systems
Invited Speaker: Courtney Paquette

ICML 2021 Workshop: Self-Supervised Learning for Reasoning and Perception
Invited Speaker: Chelsea Finn

Workshop on Reinforcement Learning Theory
Invited Speaker: Bo Dai

Tutorials (only Google affiliations are noted)
Responsible AI in Industry: Practical Challenges and Lessons Learned
Organizers: Ben Packer

Online and Non-stochastic Control
Organizers: Elad Hazan

Random Matrix Theory and ML (RMT +ML)
Organizers: Fabian Pedregosa, Jeffrey Pennington, Courntey Paquette Self-Attention for Computer Vision Organizers: Prajit Ramachandran, Ashish Vaswani

* Indicates work done while at Google

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