This week marks the beginning of the 8th International Conference on Learning Representations (ICLR 2020), a fully virtual conference focused on how one can learn meaningful and useful representations of data for machine learning. ICLR offers conference and workshop tracks, both of which include invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction and issues regarding non-convex optimization.

As a Diamond Sponsor of ICLR 2020, Google will have a strong virtual presence with over 80 publications accepted, in addition to participating on organizing committees and in workshops. If you have registered for ICLR 20202, we hope you'll watch our talks and learn about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at ICLR 2020 in the list below (Googlers highlighted in blue).

__Officers and Board Members__Includes:

*Hugo LaRochelle*

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*Samy Bengio*

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*Tara Sainath*

__Organizing Committee__Includes:

*Kevin Swersky, Timnit Gebru*

__Area Chairs__Includes:

*Balaji Lakshminarayanan*

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*Been Kim*

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*Chelsea Finn*

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*Dale Schuurmans*

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*George Tucker*

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*Honglak Lee*

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*Hossein Mobahi*

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*Jasper Snoek*

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*Justin Gilmer*

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*Katherine Heller*

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*Manaal Faruqui*

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*Michael Ryoo*

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*Nicolas Le Roux*

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*Sanmi Koyejo*

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*Sergey Levine*

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*Tara Sainath*

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*Yann Dauphin*

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*Anders Søgaard*

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*David Duvenaud*

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*Jamie Morgenstern*

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*Qiang Liu*

__Publications__SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

*(see the blog post)*

*Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, Marcin Michalski*

Differentiable Reasoning Over a Virtual Knowledge Base

*Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen*

Dynamics-Aware Unsupervised Discovery of Skills

*Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman*

GenDICE: Generalized Offline Estimation of Stationary Values

*Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans*

Mathematical Reasoning in Latent Space

*Dennis Lee, Christian Szegedy, Markus N. Rabe, Kshitij Bansal, Sarah M. Loos*

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

*Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Kevin Swersky, Mohammad Norouzi*

Adjustable Real-time Style Transfer

*Mohammad Babaeizadeh, Golnaz Ghiasi*

Are Transformers Universal Approximators of Sequence-to-sequence Functions?

*Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashankc J. Reddi, Sanjiv Kumar*

AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures

*Michael S. Ryoo, AJ Piergiovanni, Mingxing Tan, Anelia Angelova*

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

*Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan*

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

*Yeming Wen, Dustin Tran, Jimmy Ba*

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning

*(see the blog post)*

*Ali Mousavi, Lihong Li, Qiang Liu, Dengyong Zhou*

Can Gradient Clipping Mitigate Label Noise?

*Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar*

CAQL: Continuous Action Q-Learning

*Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier*

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation

*Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh*

Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization

*Satrajit Chatterjee*

Consistency Regularization for Generative Adversarial Networks

*Han Zhang, Zizhao Zhang, Augustus Odena, Honglak Lee*

Contrastive Representation Distillation

*Yonglong Tian, Dilip Krishnan, Phillip Isola*

Deep Audio Priors Emerge from Harmonic Convolutional Networks

*Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman*

Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions

*Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison Cottrell, Geoffrey Hinton*

Detecting Extrapolation with Local Ensembles

*David Madras, James Atwood, Alexander D'Amour*

Disentangling Factors of Variations Using Few Labels

*Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem*

Distance-Based Learning from Errors for Confidence Calibration

*Chen Xing, Sercan Ö. Arik, Zizhao Zhang, Tomas Pfister*

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

*(see the blog post)*

*Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning*

ES-MAML: Simple Hessian-Free Meta Learning

*(see the blog post)*

*Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Wenbo Gao, Yunhao Tang*

Exploration in Reinforcement Learning with Deep Covering Options

*Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris*

Extreme Tensoring for Low-Memory Preconditioning

*Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang*

Fantastic Generalization Measures and Where to Find Them

*Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio*

Generalization Bounds for Deep Convolutional Neural Networks

*Philip M. Long, Hanie Sedghi*

Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition

*Jongbin Ryu, GiTaek Kwon, Ming-Hsuan Yang, Jongwoo Lim*

Generative Models for Effective ML on Private, Decentralized Datasets

*Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arca*s

Generative Ratio Matching Networks

*Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton*

Global Relational Models of Source Code

*Vincent J. Hellendoorn, Petros Maniatis, Rishabh Singh, Charles Sutton, David Bieber*

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation

*Suraj Nair, Chelsea Finn*

Identity Crisis: Memorization and Generalization Under Extreme Overparameterization

*Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer*

Imitation Learning via Off-Policy Distribution Matching

*Ilya Kostrikov, Ofir Nachum, Jonathan Tompson*

Language GANs Falling Short

*Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joëlle Pineau, Laurent Charlin*

Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes

*Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh*

Learning Execution through Neural Code Fusion

*Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi*

Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

*Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia*

Learning to Learn by Zeroth-Order Oracle

*Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh*

Learning to Represent Programs with Property Signatures

*Augustus Odena, Charles Sutton*

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

*Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang*

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

*Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet*

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

*Sungryull Sohn, Hyunjae Woo, Jongwook Choi, Honglak Lee*

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

*Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle*

Model-based Reinforcement Learning for Biological Sequence Design

*Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell*

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

*Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee*

Observational Overfitting in Reinforcement Learning

*Xingyou Song, Yiding Jiang, Stephen Tu, Behnam Neyshabur, Yilun Du*

On Bonus-based Exploration Methods In The Arcade Learning Environment

*Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare*

On Identifiability in Transformers

*Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer*

On Mutual Information Maximization for Representation Learning

*Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic*

On the Global Convergence of Training Deep Linear ResNets

*Difan Zou, Philip M. Long, Quanquan Gu*

Phase Transitions for the Information Bottleneck in Representation Learning

*Tailin Wu, Ian Fischer*

Pre-training Tasks for Embedding-based Large-scale Retrieval

*Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar*

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

*Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui*

Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks

*Wei Hu, Lechao Xiao, Jeffrey Pennington*

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

*Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals*

Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

*Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals*

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

*David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel, Kihyuk Sohn*

Scalable Model Compression by Entropy Penalized Reparameterization

*Deniz Oktay, Johannes Ballé, Saurabh Singh, Abhinav Shrivastava*

Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base

*William W. Cohen, Haitian Sun, R. Alex Hofer, Matthew Siegler*

Semi-Supervised Generative Modeling for Controllable Speech Synthesis

*Raza Habib, Soroosh Mariooryad, Matt Shannon, Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, David Kao, Tom Bagby*

Span Recovery for Deep Neural Networks with Applications to Input Obfuscation

*Rajesh Jayaram, David Woodruff, Qiuyi Zhang*

Thieves on Sesame Street! Model Extraction of BERT-based APIs

*Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer*

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

*Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog*

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

*Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma*

Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards

*Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn*

Weakly Supervised Disentanglement with Guarantees

*Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole*

You Only Train Once: Loss-Conditional Training of Deep Networks

*Alexey Dosovitskiy, Josip Djolonga*

A Mutual Information Maximization Perspective of Language Representation Learning

*Lingpeng Kong, Cyprien de Masson d’Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama*

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

*(see the blog post)*

*Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut*

Asymptotics of Wide Networks from Feynman Diagrams

*Ethan Dyer, Guy Gur-Ari*

DDSP: Differentiable Digital Signal Processing

*Jesse Engel, Lamtharn Hantrakul, Chenjie Gu, Adam Roberts*

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

*Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu*

Dream to Control: Learning Behaviors by Latent Imagination

*(see the blog post)*

*Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi*

Emergent Tool Use From Multi-Agent Autocurricula

*Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch*

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

*Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi (Richard) Zhang*

HOPPITY: Learning Graph Transformations to Detect and Fix Bugs in Programs

*Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang*

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

*Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song*

Model Based Reinforcement Learning for Atari

*(see the blog post)*

*Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski*

Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension

*Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le*

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

*Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen*

Measuring the Reliability of Reinforcement Learning Algorithms

*Stephanie C.Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama*

Meta-Learning without Memorization

*Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn*

Neural Tangents: Fast and Easy Infinite Neural Networks in Python (see the blog post)

*Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz*

Scaling Autoregressive Video Models

*Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit*

The Intriguing Role of Module Criticality in the Generalization of Deep Networks

*Niladri Chatterji, Behnam Neyshabur, Hanie Sedghi*

Reformer: The Efficient Transformer

*(see the blog post)*

*Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya*

__Workshops__Computer Vision for Global Challenges

Organizing Committee:

*Ernest Mwebaze*

Advisory Committee:

*Timnit Gebru, John Quinn*

Practical ML for Developing Countries: Learning under limited/low resource scenarios

Organizing Committee:

*Nyalleng Moorosi, Timnit Gebru*

Program Committee:

*Pablo Samuel Castro, Samy Bengio*

Keynote Speaker:

*Karmel Allison*

Tackling Climate Change with Machine Learning

Organizing Committee:

*Moustapha Cisse*

Co-Organizer:

*Natasha Jaques*

Program Committee:

*John C. Platt, Kevin McCloskey, Natasha Jaques*

Advisor and Panel:

*John C. Platt*

Towards Trustworthy ML: Rethinking Security and Privacy for ML

Organizing Committee:

*Nicholas Carlini, Nicolas Papernot*

Program Committee:

*Shuang Song*