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Google at ICML 2019

Machine learning is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us to solve deep scientific and engineering challenges in areas of language, speech, translation, music, visual processing and more.

As a leader in machine learning research, Google is proud to be a Sapphire Sponsor of the thirty-sixth International Conference on Machine Learning (ICML 2019), a premier annual event supported by the International Machine Learning Society taking place this week in Long Beach, CA. With nearly 200 Googlers attending the conference to present publications and host workshops, we look forward to our continued collaboration with the larger machine learning research community.

If you’re attending ICML 2019, we hope you’ll visit the Google booth to learn more about the exciting work, creativity and fun that goes into solving some of the field’s most interesting challenges, with researchers on hand to talk about Google Research Football Environment, AdaNet, Robotics at Google and much more. You can learn more about the Google research being presented at ICML 2019 in the list below (Google affiliations highlighted in blue).

ICML 2019 Committees
Board Members include: Andrew McCallum, Corinna Cortes, Hugo Larochelle, William Cohen (Emeritus)

Senior Area Chairs include: Charles Sutton, Claudio Gentile, Corinna Cortes, Kevin Murphy, Mehryar Mohri, Nati Srebro, Samy Bengio, Surya Ganguli

Area Chairs include: Jacob Abernethy, William Cohen, Dumitru Erhan, Cho-Jui Hsieh, Chelsea Finn, Sergey Levine, Manzil Zaheer, Sergei Vassilvitskii, Boqing Gong, Been Kim, Dale Schuurmans, Danny Tarlow, Dustin Tran, Hanie Sedghi, Honglak Lee, Jasper Snoek, Lihong Li, Minmin Chen, Mohammad Norouzi, Nicolas Le Roux, Phil Long, Sanmi Koyejo, Timnit Gebru, Vitaly Feldman, Satyen Kale, Katherine Heller, Hossein Mobahi, Amir Globerson, Ilya Tolstikhin, Marco Cuturi, Sebastian Nowozin, Amin Karbasi, Ohad Shamir, Graham Taylor

Accepted Publications
Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman

Metric-Optimized Example Weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta

HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox

Learning to Clear the Market
Weiran Shen, Sebastien Lahaie, Renato Paes Leme

Shape Constraints for Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu

Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena

High-Fidelity Image Generation With Fewer Labels
Mario Lučić, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly

Learning Optimal Linear Regularizers
Matthew Streeter

DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim, Clément Chatelain, Chloe-Agathe Azencott, Jean-Philippe Vert

Learning from a Learner
Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin

Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh

An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani, Shankar Krishnan, Ying Xiao

Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli

Subspace Robust Wasserstein Distances
François-Pierre Paty, Marco Cuturi

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel Park, Jascha Sohl-Dickstein, Quoc Le, Samuel Smith

A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin

Area Attention
Yang Li, Łukasz Kaiser, Samy Bengio, Si Si

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan, Quoc Le

Static Automatic Batching In TensorFlow
Ashish Agarwal

The Evolved Transformer
David So, Quoc Le, Chen Liang

Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill

Self-similar Epochs: Value in Arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias

The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Marc G. Bellemare, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans

Adversarial Examples Are a Natural Consequence of Test Error in Noise
Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh

Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel

Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, Jon Kleinberg

A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi, Mohammad Hossein Bateni, Vahab Mirrokni

Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder

Generalized Majorization-Minimization
Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb

NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter

Variational Russian Roulette for Deep Bayesian Nonparametrics
Kai Xu, Akash Srivastava, Charles Sutton

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona

Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović

The Advantages of Multiple Classes for Reducing Overfitting from Test Set Reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt

Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi

Hiring Under Uncertainty
Manish Purohit, Sreenivas Gollapudi, Manish Raghavan

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii

Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney

Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest

Active Learning with Disagreement Graphs
Corinna Cortes, Giulia DeSalvo,, Mehryar Mohri, Ningshan Zhang, Claudio Gentile

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

Understanding the Impact of Entropy on Policy Optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans

Matrix-Free Preconditioning in Online Learning
Ashok Cutkosky, Tamas Sarlos

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer

Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Kareem Amin, Alex Kulesza, Andres Munoz Medina, Sergei Vassilvtiskii

Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama

Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller

Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Online Learning with Sleeping Experts and Feedback Graphs
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang

Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow

Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh

Adversarial Online Learning with Noise
Alon Resler, Yishay Mansour

Escaping Saddle Points with Adaptive Gradient Methods
Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

Fairness Risk Measures
Robert Williamson, Aditya Menon

DBSCAN++: Towards Fast and Scalable Density Clustering
Jennifer Jang, Heinrich Jiang

Learning Linear-Quadratic Regulators Efficiently with only √T Regret
Alon Cohen, Tomer Koren, Yishay Mansour

Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein

Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly

Efficient Full-Matrix Adaptive Regularization
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

Efficient On-Device Models Using Neural Projections
Sujith Ravi

Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

Recursive Sketches for Modular Deep Learning
Badih Ghazi, Rina Panigrahy, Joshua Wang

POLITEX: Regret Bounds for Policy Iteration Using Expert Prediction
Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazić, Csaba Szepesvári, Gellért Weisz

Anytime Online-to-Batch, Optimism and Acceleration
Ashok Cutkosky

Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit

Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

A Better k-means++ Algorithm via Local Search
Silvio Lattanzi, Christian Sohler

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton

Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
Eric Chu, Peter Liu

CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit

Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton

Online Algorithms for Rent-Or-Buy with Expert Advice
Sreenivas Gollapudi, Debmalya Panigrahi

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian Ganea, Sylvain Gelly, Gary Becigneul, Aliaksei Severyn

Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam

Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh

Categorical Feature Compression via Submodular Optimization
Mohammad Hossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh

Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia

Faster Algorithms for Binary Matrix Factorization
Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff

On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, George Tucker

Guided Evolutionary Strategies: Augmenting Random Search with Surrogate Gradients
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein

Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar

Workshops
1st Workshop on Understanding and Improving Generalization in Deep Learning
Organizers Include: Dilip Krishnan, Hossein Mobahi
Invited Speaker: Chelsea Finn

Climate Change: How Can AI Help?
Invited Speaker: John Platt

Generative Modeling and Model-Based Reasoning for Robotics and AI
Organizers Include: Dumitru Erhan, Sergey Levine, Kimberly Stachenfeld
Invited Speaker: Chelsea Finn

Human In the Loop Learning (HILL)
Organizers Include: Been Kim

ICML 2019 Time Series Workshop
Organizers Include: Vitaly Kuznetsov

Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR)
Organizers Include: Sujith Ravi, Zornitsa Kozareva

Negative Dependence: Theory and Applications in Machine Learning
Organizers Include: Jennifer Gillenwater, Alex Kulesza

Reinforcement Learning for Real Life
Organizers Include: Lihong Li
Invited Speaker: Craig Boutilier

Uncertainty and Robustness in Deep Learning
Organizers Include: Justin Gilmer

Theoretical Physics for Deep Learning
Organizers Include: Jaehoon Lee, Jeffrey Pennington, Yasaman Bahri

Workshop on the Security and Privacy of Machine Learning
Organizers Include: Nicolas Papernot
Invited Speaker: Been Kim

Exploration in Reinforcement Learning Workshop
Organizers Include: Benjamin Eysenbach, Surya Bhupatiraju, Shixiang Gu

ICML Workshop on Imitation, Intent, and Interaction (I3)
Organizers Include: Sergey Levine, Chelsea Finn
Invited Speaker: Pierre Sermanet

Identifying and Understanding Deep Learning Phenomena
Organizers Include: Hanie Sedghi, Samy Bengio, Kenji Hata, Maithra Raghu, Ali Rahimi, Ying Xiao

Workshop on Multi-Task and Lifelong Reinforcement Learning
Organizers Include: Sarath Chandar, Chelsea Finn
Invited Speakers: Karol Hausman, Sergey Levine

Workshop on Self-Supervised Learning
Organizers Include: Pierre Sermanet

Invertible Neural Networks and Normalizing Flows
Organizers Include: Rianne Van den Berg, Danilo J. Rezende
Invited Speakers: Eric Jang, Laurent Dinh

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.