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 Platinum Sponsor of the thirty-seventh International Conference on Machine Learning (ICML 2020), a premier annual event taking place virtually this week. With over 100 accepted publications and Googlers participating in workshops, we look forward to our continued collaboration with the larger machine learning research community.

If you're registered for ICML 2020, we hope you'll visit the Google virtual booth to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges. You can also learn more about the Google research being presented at ICML 2020 in the list below (Google affiliations

**bolded**).

__ICML Expo__Google Dataset Search: Building an Open Ecosystem for Dataset Discovery

*Natasha Noy*End-to-end Bayesian inference workflows in TensorFlow Probability

*Colin Carroll*

__Publications__Population-Based Black-Box Optimization for Biological Sequence Design

*Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley*Predictive Coding for Locally-Linear Control

*Rui Shu, Tung Nguyen,*

**Yinlam Chow**, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung BuiFedBoost: A Communication-Efficient Algorithm for Federated Learning

*Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh*Faster Graph Embeddings via Coarsening

*Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang*Revisiting Fundamentals of Experience Replay

**William Fedus, Prajit Ramachandran, Rishabh Agarwal,**Yoshua Bengio**, Hugo Larochelle,**Mark Rowland, Will DabneyBoosting for Control of Dynamical Systems

*Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu*Neural Clustering Processes

*Ari Pakman,*

**Yueqi Wang**, Catalin Mitelut, JinHyung Lee, Liam PaninskiThe Tree Ensemble Layer: Differentiability Meets Conditional Computation

*Hussein Hazimeh,*

**Natalia Ponomareva**,**Petros Mol, Zhenyu Tan**, Rahul MazumderRepresentations for Stable Off-Policy Reinforcement Learning

*Dibya Ghosh, Marc Bellemare*REALM: Retrieval-Augmented Language Model Pre-Training

*Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang*Context Aware Local Differential Privacy

*Jayadev Acharya,*

**Keith Bonawitz**,**Peter Kairouz**,**Daniel Ramage**,**Ziteng Sun**Scalable Deep Generative Modeling for Sparse Graphs

**Hanjun Dai, Azade Nazi,**Yujia Li**, Bo Dai, Dale Schuurmans**Deep k-NN for Noisy Labels

*Dara Bahri, Heinrich Jiang, Maya Gupta*^{†}Revisiting Spatial Invariance with Low-Rank Local Connectivity

*Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith*SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

*Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh*Incremental Sampling Without Replacement for Sequence Models

*Kensen Shi, David Bieber, Charles Sutton*SoftSort: A Continuous Relaxation for the argsort Operator

*Sebastian Prillo,*

**Julian Martin Eisenschlos**XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation

*(see blog post)*

*Junjie Hu, Sebastian Ruder,*

**Aditya Siddhant**, Graham Neubig,**Orhan Firat**,**Melvin Johnson**Learning to Stop While Learning to Predict

*Xinshi Chen,*

**Hanjun Dai**, Yu Li, Xin Gao, Le SongBandits with Adversarial Scaling

*Thodoris Lykouris,*

**Vahab Mirrokni**,**Renato Paes Leme**SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

*Tomer Golany,*

**Daniel Freedman**, Kira RadinskyStochastic Frank-Wolfe for Constrained Finite-Sum Minimization

*Geoffrey Negiar, Gideon Dresdner, Alicia Yi-Ting Tsai, Laurent El Ghaoui, Francesco Locatello, Robert M. Freund,*

**Fabian Pedregosa**Implicit differentiation of Lasso-type models for hyperparameter optimization

*Quentin Bertrand, Quentin Klopfenstein,*

**Mathieu Blondel**, Samuel Vaiter, Alexandre Gramfort, Joseph SalmonInfinite attention: NNGP and NTK for deep attention networks

*Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak*Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently

*Asaf Cassel,*

**Alon Cohen**, Tomer KorenAdversarial Learning Guarantees for Linear Hypotheses and Neural Networks

**Pranjal Awasthi,**Natalie Frank**, Mehryar Mohri**Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

*Daniel Golovin, Qiuyi (Richard) Zhang*Generating Programmatic Referring Expressions via Program Synthesis

*Jiani Huang, Calvin Smith, Osbert Bastani,*

**Rishabh Singh**, Aws Albarghouthi, Mayur NaikOptimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

**Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky,**Richard Zemel**, Craig Boutilier**AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

*(see blog post)*

*Esteban Real, Chen Liang, David R. So, Quoc V. Le*How Good is the Bayes Posterior in Deep Neural Networks Really?

*Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin*^{†}Which Tasks Should Be Learned Together in Multi-task Learning?

*Trevor Standley, Amir R. Zamir,*

**Dawn Chen**, Leonidas Guibas, Jitendra Malik, Silvio SavareseInfluence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems

*Tong Yu,*

**Branislav Kveton**, Zheng Wen, Ruiyi Zhang, Ole J. MengshoelDisentangling Trainability and Generalization in Deep Neural Networks

*Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz*The Many Shapley Values for Model Explanation

*Mukund Sundararajan, Amir Najmi*Neural Contextual Bandits with UCB-based Exploration

*Dongruo Zhou,*

**Lihong Li**, Quanquan GuAutomatic Shortcut Removal for Self-Supervised Representation Learning

*Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen*Federated Learning with Only Positive Labels

*Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar*How Recurrent Networks Implement Contextual Processing in Sentiment Analysis

*Niru Maheswaranathan, David Sussillo*Supervised Learning: No Loss No Cry

*Richard Nock,*

**Aditya Krishna Menon**Ready Policy One: World Building Through Active Learning

*Philip Ball, Jack Parker-Holder, Aldo Pacchiano,*

**Krzysztof Choromanski**, Stephen RobertsWeakly-Supervised Disentanglement Without Compromises

*Francesco Locatello,*

**Ben Poole**, Gunnar Raetsch, Bernhard Schölkopf,**Olivier Bachem**,**Michael Tschannen**Fast Differentiable Sorting and Ranking

*Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga*Debiased Sinkhorn barycenters

*Hicham Janati,*

**Marco Cuturi**, Alexandre GramfortInterpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure

*John Sipple*Accelerating Large-Scale Inference with Anisotropic Vector Quantization

*Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng*^{†}, David Simcha, Felix Chern, Sanjiv KumarAn Optimistic Perspective on Offline Reinforcement Learning

*(see blog post)*

*Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi*The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

*Ben Adlam, Jeffrey Pennington*Private Query Release Assisted by Public Data

*Raef Bassily, Albert Cheu,*

**Shay Moran**, Aleksandar Nikolov, Jonathan Ullman, Zhiwei Steven WuLearning and Evaluating Contextual Embedding of Source Code

*Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi*Evaluating Machine Accuracy on ImageNet

*Vaishaal Shankar,*

**Rebecca Roelofs**, Horia Mania, Alex Fang, Benjamin Recht, Ludwig SchmidtImputer: Sequence Modelling via Imputation and Dynamic Programming

*William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly*Domain Aggregation Networks for Multi-Source Domain Adaptation

*Junfeng Wen, Russell Greiner,*

**Dale Schuurmans**Planning to Explore via Self-Supervised World Models

*Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel,*

**Danijar Hafner**, Deepak PathakContext-Aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

*Kimin Lee, Younggyo Seo, Seunghyun Lee,*

**Honglak Lee**, Jinwoo ShinRetro*: Learning Retrosynthetic Planning with Neural Guided A* Search

*Binghong Chen, Chengtao Li,*

**Hanjun Dai**, Le SongOn the Consistency of Top-k Surrogate Losses

**,**

*Forest Yang***Sanmi Koyejo**

Dual Mirror Descent for Online Allocation Problems

*Haihao Lu, Santiago Balseiro,*

**Vahab Mirrokni**Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

*Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma*^{†}, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin TranBatch Stationary Distribution Estimation

*Junfeng Wen,*

**Bo Dai**,**Lihong Li**,**Dale Schuurmans**Small-GAN: Speeding Up GAN Training Using Core-Sets

*Samarth Sinha,*

**Han Zhang**, Anirudh Goyal, Yoshua Bengio,**Hugo Larochelle**,**Augustus Odena**Data Valuation Using Reinforcement Learning

*Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister*A Game Theoretic Perspective on Model-Based Reinforcement Learning

*Aravind Rajeswaran, Igor Mordatch, Vikash Kumar*Encoding Musical Style with Transformer Autoencoders

*Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel*The Shapley Taylor Interaction Index

*Kedar Dhamdhere, Mukund Sundararajan, Ashish Agarwal*Multidimensional Shape Constraints

*Maya Gupta*^{†}, Erez Louidor, Olexander Mangylov^{†}, Nobu Morioka, Taman Narayan, Sen ZhaoPrivate Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead

*Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh*Learning to Score Behaviors for Guided Policy Optimization

*Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska,*,

**Krzysztof Choromanski***Michael I. Jordan*

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

*Florian Tramèr, Jens Behrmann,*

**Nicholas Carlini**,**Nicolas Papernot**, Jörn-Henrik JacobsenOptimizing Black-Box Metrics with Adaptive Surrogates

*Qijia Jiang, Olaoluwa Adigun,*

**Harikrishna Narasimhan**,**Mahdi Milani Fard**,**Maya Gupta**^{†}Circuit-Based Intrinsic Methods to Detect Overfitting

**Sat Chatterjee,**Alan MishchenkoAutomatic Reparameterisation of Probabilistic Programs

*Maria I. Gorinova, Dave Moore, Matthew D. Hoffman*Stochastic Flows and Geometric Optimization on the Orthogonal Group

**Krzysztof Choromanski,**David Cheikhi**, Jared Davis**, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou,**Xingyou Song**, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano,**Tamas Sarlos**, Adrian Weller,**Vikas Sindhwani**Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics

*Matthew Hoffman, Yi-An Ma*^{†}Concise Explanations of Neural Networks Using Adversarial Training

*Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha,*

**Xi Wu**p-Norm Flow Diffusion for Local Graph Clustering

*Shenghao Yang,*

**Di Wang**, Kimon FountoulakisEmpirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

*Rares-Darius Buhai,*

**Yoni Halpern**, Yoon Kim, Andrej Risteski, David SontagRobust Pricing in Dynamic Mechanism Design

*Yuan Deng,*

**Sébastien Lahaie**,**Vahab Mirrokni**Differentiable Product Quantization for Learning Compact Embedding Layers

**Ting Chen, Lala Li,**Yizhou SunAdaptive Region-Based Active Learning

**Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri,**Ningshan ZhangCountering Language Drift with Seeded Iterated Learning

*Yuchen Lu, Soumye Singhal, Florian Strub,*

**Olivier Pietquin**, Aaron CourvilleDoes Label Smoothing Mitigate Label Noise?

*Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar*Acceleration Through Spectral Density Estimation

**Fabian Pedregosa,**Damien ScieurMomentum Improves Normalized SGD

*Ashok Cutkosky, Harsh Mehta*ConQUR: Mitigating Delusional Bias in Deep Q-Learning

*Andy Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier*Online Learning with Imperfect Hints

*Aditya Bhaskara,*

**Ashok Cutkosky**,**Ravi Kumar**,**Manish Purohit**Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

**Denny Zhou,**Mao Ye**, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le,**Qiang Liu**, Dale Schuurmans**On Implicit Regularization in β-VAEs

*Abhishek Kumar, Ben Poole*Is Local SGD Better than Minibatch SGD?

*Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins,*

**H. Brendan McMahan**, Ohad Shamir, Nathan SrebA Simple Framework for Contrastive Learning of Visual Representations

*Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton*Universal Average-Case Optimality of Polyak Momentum

*Damien Scieur,*

**Fabian Pedregosa**An Imitation Learning Approach for Cache Replacement

*Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn*Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

**Zhe Dong, Bryan A. Seybold, Kevin P. Murphy,**Hung H. BuiBeyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

**Lu Jiang, Di Huang,**Mason Liu,**Weilong Yang**Optimizing Data Usage via Differentiable Rewards

*Xinyi Wang,*

**Hieu Pham**, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham NeubigSparse Sinkhorn Attention

*Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, Da-Cheng Juan*One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

*Wenlong Huang,*

**Igor Mordatch**, Deepak PathakOn Thompson Sampling with Langevin Algorithms

*Eric Mazumdar, Aldo Pacchiano,*

**Yi-An Ma**, Peter L. Bartlett, Michael I. Jordan^{†}Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

*Mao Ye, Chengyue Gong, Lizhen Nie,*

**Denny Zhou**, Adam Klivans, Qiang LiuOn the Global Convergence Rates of Softmax Policy Gradient Methods

**Jincheng Mei,**Chenjun Xiao, Csaba Szepesvari**, Dale Schuurmans**Concept Bottleneck Models

*Pang Wei Koh,*

**Thao Nguyen**, Yew Siang Tang, Stephen Mussmann, Emma Pierson,**Been Kim**, Percy LiangSupervised Quantile Normalization for Low-Rank Matrix Approximation

**Marco Cuturi, Olivier Teboul,**Jonathan Niles-Weed**, Jean-Philippe Vert**Missing Data Imputation Using Optimal Transport

*Boris Muzellec,*

**Julie Josse**, Claire Boyer,**Marco Cuturi**Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention Over Modules

*Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie,*

**Michael Mozer**, Yoshua BengioStochastic Optimization for Regularized Wasserstein Estimators

*Marin Ballu,*

**Quentin Berthet**, Francis BachLow-Rank Bottleneck in Multi-head Attention Models

*Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Jakkam Reddi, Sanjiv Kumar*Rigging the Lottery: Making All Tickets Winners

**Utku Evci, Trevor Gale,**Jacob Menick**, Pablo Samuel Castro, Erich Elsen**Online Learning with Dependent Stochastic Feedback Graphs

**Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri,**Ningshan ZhangCalibration, Entropy Rates, and Memory in Language Models

*Mark Braverman,*

**Xinyi Chen**, Sham Kakade, Karthik Narasimhan, Cyril Zhang, Yi ZhangComposable Sketches for Functions of Frequencies: Beyond the Worst Case

**Edith Cohen, Ofir Geri,**Rasmus PaghEnergy-Based Processes for Exchangeable Data

*Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans*Near-Optimal Regret Bounds for Stochastic Shortest Path

**Alon Cohen, Haim Kaplan, Yishay Mansour,**Aviv RosenbergPEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

*(see blog post)*

*Jingqing Zhang,*

**Yao Zhao**,**Mohammad Saleh**,**Peter J. Liu**The Complexity of Finding Stationary Points with Stochastic Gradient Descent

*Yoel Drori, Ohad Shamir*The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

*Jakub Swiatkowski, Kevin Roth, Bas Veeling, Linh Tran, Josh Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin*^{†}Regularized Optimal Transport is Ground Cost Adversarial

*François-Pierre Paty,*

**Marco Cuturi**

__Workshops__New In ML

Invited Speaker:

**Nicolas Le Roux**Organizers:

*Zhen Xu, Sparkle Russell-Puleri, Zhengying Liu, Sinead A Williamson, Matthias W Seeger, Wei-Wei Tu,*

**Samy Bengio**, Isabelle GuyonLatinX in AI

Workshop Advisor:

**Pablo Samuel Castro**Women in Machine Learning Un-Workshop

Invited Speaker:

*Doina Precup*Sponsor Expo Speaker:

**Jennifer Wei**Queer in AI

Invited Speaker:

**Shakir Mohamed**Workshop on Continual Learning

Organizers:

*Haytham Fayek, Arslan Chaudhry, David Lopez-Paz, Eugene Belilovsky, Jonathan Schwarz,*

**Marc Pickett**, Rahaf Aljundi, Sayna Ebrahimi, Razvan Pascanu, Puneet Dokania5th ICML Workshop on Human Interpretability in Machine Learning (WHI)

Organizers:

*Kush Varshney, Adrian Weller, Alice Xiang, Amit Dhurandhar,*

**Been Kim**, Dennis Wei, Umang BhattSelf-supervision in Audio and Speech

Organizers:

*Mirco Ravanelli, Dmitriy Serdyuk, R Devon Hjelm,*

**Bhuvana Ramabhadran**, Titouan ParcolletWorkshop on eXtreme Classification: Theory and Applications

Invited Speakers:

*Sanjiv Kumar*Healthcare Systems, Population Health, and the Role of Health-tech

Organizers:

**Krzysztof Choromanski**, David Cheikhi,**Jared Davis**, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou,**Xingyou Song**, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano,**Tamas Sarlos**, Adrian Weller,**Vikas Sindhwani**Theoretical Foundations of Reinforcement Learning

Program Committee:

*Alon Cohen, Chris Dann*Uncertainty and Robustness in Deep Learning Workshop (UDL)

Invited Speaker:

**Justin Gilmer**Organizers:

*Sharon Li,*

**Balaji Lakshminarayanan**, Dan Hendrycks, Thomas Dietterich,**Jasper Snoek**Program Committee:

**Jeremiah Liu**,**Jie Ren, Rodolphe Jenatton**,**Zack Nado**,**Alexander Alemi**,**Florian Wenzel**,**Mike Dusenberry**,**Raphael Lopes**Beyond First Order Methods in Machine Learning Systems

Industry Panel:

**Jonathan Hseu**Object-Oriented Learning: Perception, Representation, and Reasoning

Invited Speakers:

**Thomas Kipf**,**Igor Mordatch**Graph Representation Learning and Beyond (GRL+)

Organizers:

*Michael Bronstein, Andreea Deac, William L. Hamilton, Jessica B. Hamrick,*

**Milad Hashemi**, Stefanie Jegelka, Jure Leskovec, Renjie Liao, Federico Monti, Yizhou Sun,**Kevin Swersky**, Petar Veličković, Rex Ying, Marinka ŽitnikSpeakers:

**Thomas Kipf**Program Committee:

**Bryan Perozzi**,**Kevin Swersky**,**Milad Hashemi**,**Thomas Kipf**,**Ting Cheng**ML Interpretability for Scientific Discovery

Organizers:

**Subhashini Venugopalan**,**Michael Brenner**, Scott Linderman,**Been Kim**Program Committee:

**Akinori Mitani**,**Arunachalam Narayanaswamy**,**Avinash Varadarajan**,**Awa Dieng**,**Benjamin Sanchez-Lengeling**,**Bo Dai**,**Stephan Hoyer**,**Subham Sekhar Sahoo**,**Suhani Vora**Steering Committee:

**John Platt, Mukund Sundararajan**, Jon KleinbergNegative Dependence and Submodularity for Machine Learning

Organizers:

**Zelda Mariet**, Mike Gartrell, Michal Derezinski7th ICML Workshop on Automated Machine Learning (AutoML)

Organizers:

**Charles Weill**, Katharina Eggensperger, Matthias Feurer, Frank Hutter, Marius Lindauer, Joaquin VanschorenFederated Learning for User Privacy and Data Confidentiality

Keynote:

**Brendan McMahan**Program Committee:

**Peter Kairouz**,**Jakub Konecný**MLRetrospectives: A Venue for Self-Reflection in ML Research

Speaker:

**Margaret Mitchell**Machine Learning for Media Discovery

Speaker:

*Ed Chi*INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models

Organizers

*: Chin-Wei Huang, David Krueger,*

**Rianne van den Berg**, George Papamakarios, Chris Cremer, Ricky Chen, Danilo Rezende4th Lifelong Learning Workshop

Program Committee:

**George Tucker**,**Marlos C. Machado**2nd ICML Workshop on Human in the Loop Learning (HILL)

*Organizers: Shanghang Zhang, Xin Wang, Fisher Yu,*

**Jiajun Wu**, Trevor DarrellMachine Learning for Global Health

Organizers

*: Danielle Belgrave, Danielle Belgrave, Stephanie Hyland, Charles Onu, Nicholas Furnham,*

**Ernest Mwebaze**, Neil Lawrence

__Committee__Social Chair:

*Adam White*

^{†}Work performed while at Google