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Google at NeurIPS 2019

Posted by Andrew Helton, Editor, Google Research Communications

This week, Vancouver hosts the 33rd annual Conference on Neural Information Processing Systems (NeurIPS 2019), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. As a Diamond Sponsor of NeurIPS 2019, Google will have a strong presence at NeurIPS 2019 with more than 500 Googlers attending in order to contribute to, and learn from, the broader academic research community via talks, posters, workshops, competitions and tutorials. We will be presenting work that pushes the boundaries of what is possible in language understanding, translation, speech recognition and visual & audio perception, with Googlers co-authoring more than 120 accepted papers.

If you are attending NeurIPS 2019, we hope you’ll stop by our 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 ML-based Flood Forecasting, AI for Social Good, Google Research Football, Google Dataset Search, TF-Agents and much more. You can also learn more about our work being presented in the list below (Google affiliations highlighted in blue).

NeurIPS Foundation Board
Samy Bengio, Corinna Cortes

NeurIPS Advisory Board
John C. Platt, Fernando Pereira, Dale Schuurmans

NeurIPS Program Committee
Program Chair: Hugo Larochelle
Diversity & Inclusion Co-Chair: Katherine Heller
Meetup Chair: Nicolas La Roux
Party Co-Chair: Pablo Samuel Castro

Senior Area Chairs include: Amir Globerson, Claudio Gentile, Cordelia Schmid, Corinna Cortes, Dale Schuurmans, Elad Hazan, Honglak Lee, Mehryar Mohri, Peter Bartlett, Satyen Kale, Sergey Levine, Surya Ganguli

Area Chairs include: Afshin Rostamizadeh, Alex Kulesza, Amin Karbasi, Andrew Dai, Been Kim, Boqing Gong, Brainslav Kveton, Ce Liu, Charles Sutton, Chelsea Finn, Cho-Jui Hsieh, D Sculley, Danny Tarlow, David Held, Denny Zhou, Yann Dauphin, Dustin Tran, Hartmut Neven, Hossein Mobahi, Ilya Tolstikhin, Jasper Snoek, Jean-Philippe Vert, Jeffrey Pennington, Kevin Swersky, Kun Zhang, Kunal Talwar, Lihong Li, Manzil Zaheer, Marc G Bellemare, Marco Cuturi, Maya Gupta, Meg Mitchell, Minmin Chen, Mohammad Norouzi, Moustapha Cisse, Olivier Bachem, Qiang Liu, Rong Ge, Sanjiv Kumar, Sanmi Koyejo, Sebastian Nowozin, Sergei Vassilvitskii, Shivani Agarwal, Slav Petrov, Srinadh Bhojanapalli, Stephen Bach, Timnit Gebru, Tomer Koren, Vitaly Feldman, William Cohen, Yann Dauphin, Nicolas La Roux

NeurIPS Workshops Program Committee
Yann Dauphin, Honglak Lee, Sebastian Nowozin, Fernanda Viegas

NeurIPS Invited Talk
Social Intelligence
Blaise Aguera y Arcas

Accepted Papers
Memory Efficient Adaptive Optimation
Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

Stand-Alone Self-Attention in Vision Models
Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jon Shlens

High Fidelity Video Prediction with Large Neural Nets
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee

Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin Murphy, Honglak Lee

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, Hyouk Joong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen

Quadratic Video Interpolation
Xiangyu Xu, Li Si-Yao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang

Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Aviv Rosenberg, Yishay Mansour

Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
Yogev Bar-On, Yishay Mansour

Learning to Screen
Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li

A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Liu, John Paisley, Marithani-Anna Kioumourtzoglou, Brent Coull

Saccader: Improving Accuracy of Hard Attention Models for Vision
Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le

Invertible Convolutional Flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth

Hypothesis Set Stability and Generalization
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Bandits with Feedback Graphs and Switching Costs
Raman Arora, Teodor V. Marinov, Mehryar Mohri

Regularized Gradient Boosting
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus

Logarithmic Regret for Online Control
Naman Agarwal, Elad Hazan, Karan Singh

Sampled Softmax with Random Fourier Features
Ankit Singh Rawat, Jiecao Chen, Felix Yu, Ananda Theertha Suresh, Sanjiv Kumar

Multilabel Reductions: What is My Loss Optimising?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

MetaInit: Initializing Learning by Learning to Initialize
Yann N. Dauphin, Sam Schoenholz

Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot

Variance Reduction of Bipartite Experiments through Correlation Clustering
Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Jessie Ren, D. Sculley, Josh Dillon, Sebastian Nowozin, Zack Nado, Balaji Lakshminarayanan, Jasper Snoek

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans

Globally Optimal Learning for Structured Elliptical Losses
Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel

DPPNet: Approximating Determinantal Point Processes with Deep Networks
Zelda Mariet, Yaniv Ovadia, Jasper Snoek

Graph Normalizing Flows
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

When Does Label Smoothing Help?
Rafael Muller, Simon Kornblith, Geoff Hinton

On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

Don’t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
James Lucas, George Tucker, Roger Grosse, Mohammad Norouzi

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

Optimizing Generalized Rate Metrics with Game Equilibrium
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

On Making Stochastic Classifiers Deterministic
Andrew Cotter, Harikrishna Narasimhan, Maya Gupta

Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

Graph Agreement Models for Semi-Supervised Learning
Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Andrew Tomkins, Sujith Ravi

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy (Dj) Dvijotham, Alhusein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

Online Learning via the Differential Privacy Lens
Jacob Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

Reducing the Variance in Online Optimization by Transporting Past Gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks
Niru Maheswaranathan, Alex Williams, Matt Golub, Surya Ganguli, David Sussillo

Reverse Engineering Recurrent Networks for Sentiment Classification Reveals Line Attractor Dynamics
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Strategizing Against No-Regret Learners
Yuan Deng, Jon Schneider, Balasubramanian Sivan

Prior-Free Dynamic Auctions with Low Regret Buyers
Yuan Deng, Jon Schneider, Balasubramanian Sivan

Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta

Computational Separations between Sampling and Optimization
Kunal Talwar

Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky and Francesco Orabona

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, Jose Miguel Hernandez-Lobato, Cheng Zhang

Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas

Visualizing and Measuring the Geometry of BERT
Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda Viégas, Martin Wattenberg

Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni

A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-jan Kindermans, Been Kim

Practical and Consistent Estimation of f-Divergences
Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin

Tree-Sliced Variants of Wasserstein Distances
Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi

Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep Ravikumar, Tom M Mitchell, William Cohen

Differentially Private Anonymized Histograms
Ananda Theertha Suresh

Locally Private Gaussian Estimation
Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
C. Daniel Freeman, Luke Metz, David Ha

Adaptive Density Estimation for Generative Models
Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek

Weight Agnostic Neural Networks
Adam Gaier, David Ha

Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song

Large Scale Structure of Neural Network Loss Landscapes
Stanislav Fort, Stainslaw Jastrzebski

Off-Policy Evaluation via Off-Policy Classification
Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine

Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
Aleksis Pirinen, Erik Gartner, Cristian Sminchisescu

Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath

From Deep Learning to Mechanistic Understanding in Neuroscience: The Structure of Retinal Prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli

Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn

Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy Mann, Andre Barreto, Sylvain Gelly, Gergely Neu

A Unified Framework for Data Poisoning Attack to Graph-based Semi-Supervised Learning
Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh

MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow (work done while at Google), Avital Oliver, Nicolas Papernot, Colin Raffel

SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
Seyed Kamyar Seyed Ghasemipour, Shixiang (Shane) Gu, Richard Zemel

Limits of Private Learning with Access to Public Data
Noga Alon, Raef Bassily, Shay Moran

Regularized Weighted Low Rank Approximation
Frank Ban, David Woodruff, Richard Zhang

Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Benjamin Eysenbach, Sergey Levine, Chelsea Finn

Secretary Ranking with Minimal Inversions
Sepehr Assadi, Eric Balkanski, Renato Paes Leme

Mixtape: Breaking the Softmax Bottleneck Efficiently
Zhilin Yang, Thang Luong, Russ Salakhutdinov, Quoc V. Le

Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang

Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot

Flattening a Hierarchical Clustering through Active Learning
Fabio Vitale, Anand Rajagopalan, Claudio Gentile

Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha

Robustness Verification of Tree-based Models
Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh

Meta Architecture Search
Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Contextual Bandits with Cross-Learning
Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Negin Golrezaei, Adel Javanmard, Vahab Mirrokni

Optimizing Generalized Rate Metrics with Three Players
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

Noise-Tolerant Fair Classification
Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon, Nakul Verma

Towards Automatic Concept-based Explanations
Amirata Ghorbani, James Wexler, James Zou, Been Kim

Locally Private Learning without Interaction Requires Separation
Amit Daniely, Vitaly Feldman

Learning GANs and Ensembles Using Discrepancy
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang

CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam

A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren

When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, Geoffrey Hinton

Memory Efficient Adaptive Optimization
Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington

Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Abstract Reasoning with Distracting Features
Kecheng Zheng, Zheng-Jun Zha, Wei Wei

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Differentiable Ranking and Sorting Using Optimal Transport
Marco Cuturi, Olivier Teboul, Jean-Philippe Vert

XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le

Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen, Elad Hazan, Shay Moran

Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song

Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Michela Meister, Tamas Sarlos, David P. Woodruff

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms
Max Vladymyrov

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Boris Muzellec, Marco Cuturi

Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Aviv Rosenberg, Yishay Mansour

Private Learning Implies Online Learning: An Efficient Reduction
Alon Gonen, Elad Hazan, Shay Moran

On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Ðorde Miladinovíc, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

Stacked Capsule Autoencoders
Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton

Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet

Sampling Sketches for Concave Sublinear Functions of Frequencies
Edith Cohen, Ofir Geri

Hamiltonian Neural Networks
Sam Greydanus, Misko Dzamba, Jason Yosinski

Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, Frédo Durand

Quadratic Video Interpolation
Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang

Transfusion: Understanding Transfer Learning for Medical Imagings
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, Samy Bengio

XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le

Differentially Private Covariance Estimation
Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta

Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli

Neural Attribution for Semantic Bug-Localization in Student Programs
Rahul Gupta, Aditya Kanade, Shirish Shevade

PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar

Efficient Rematerialization for Deep Networks
Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang

Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky, Francesco Orabona

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona

Workshops
3rd Conversational AI: Today's Practice and Tomorrow's Potential
Organizers include: Bill Byrne

AI for Humanitarian Assistance and Disaster Response Workshop
Invited Speakers include: Yossi Matias

Bayesian Deep Learning
Organizers include: Kevin P Murphy

Beyond First Order Methods in Machine Learning Systems
Invited Speakers include: Elad Hazan

Biological and Artificial Reinforcement Learning
Invited Speakers include: Igor Mordatch

Context and Compositionality in Biological and Artificial Neural Systems
Invited Speakers include: Kenton Lee

Deep Reinforcement Learning
Organizers include: Chelsea Finn

Document Intelligence
Organizers include: Tania Bedrax Weiss

Federated Learning for Data Privacy and Confidentiality
Organizers include: Jakub Konečný, Brendan McMahan
Invited Speakers include: Françoise Beaufays, Daniel Ramage

Graph Representation Learning
Organizers include: Rianne van den Berg

Human-Centric Machine Learning
Invited Speakers include: Been Kim

Information Theory and Machine Learning
Organizers include: Ben Poole
Invited Speakers include: Alex Alemi

KR2ML - Knowledge Representation and Reasoning Meets Machine Learning
Invited Speakers include: William Cohen

Learning Meaningful Representations of Life
Organizers include: Jasper Snoek, Alexander Wiltschko

Learning Transferable Skills
Invited Speakers include: David Ha

Machine Learning for Creativity and Design
Organizers include: Adam Roberts, Jesse Engel

Machine Learning for Health (ML4H): What Makes Machine Learning in Medicine Different?
Invited Speakers include: Lily Peng, Alan Karthikesalingam, Dale Webster

Machine Learning and the Physical Sciences
Speakers include: Yasaman Bahri, Samual Schoenholz

ML for Systems
Organizers include: Milad Hashemi, Kevin Swersky, Azalia Mirhoseini, Anna Goldie
Invited Speakers include: Jeff Dean

Optimal Transport for Machine Learning
Organizers include: Marco Cuturi

The Optimization Foundations of Reinforcement Learning
Organizers include: Bo Dai, Nicolas Le Roux, Lihong Li, Dale Schuurmans

Privacy in Machine Learning
Invited Speakers include: Brendan McMahan

Program Transformations for ML
Organizers include: Pascal Lamblin, Alexander Wiltschko, Bart van Merrienboer, Emily Fertig
Invited Speakers include: Skye Wanderman-Milne

Real Neurons & Hidden Units: Future Directions at the Intersection of Neuroscience and Artificial Intelligence
Organizers include: David Sussillo

Robot Learning: Control and Interaction in the Real World
Organizers include: Stefan Schaal

Safety and Robustness in Decision Making
Organizers include: Yinlam Chow

Science Meets Engineering of Deep Learning
Invited Speakers include: Yasaman Bahri, Surya Ganguli‎, Been Kim, Surya Ganguli

Sets and Partitions
Organizers include: Manzil Zaheer, Andrew McCallum
Invited Speakers include: Amr Ahmed

Tackling Climate Change with ML
Organizers include: John Platt
Invited Speakers include: Jeff Dean

Visually Grounded Interaction and Language
Invited Speakers include: Jason Baldridge

Workshop on Machine Learning with Guarantees
Invited Speakers include: Mehryar Mohri

Tutorials
Representation Learning and Fairness
Organizers include: Moustapha Cisse, Sanmi Koyejo

The increasing pace of modern life -- and how we can adapt | Kathryn Bouskill

The increasing pace of modern life -- and how we can adapt | Kathryn Bouskill

Why does modern technology promise efficiency, but leave us constantly feeling pressed for time? Anthropologist Kathryn Bouskill explores the paradoxes of living in a fast-paced society and explains why we need to reconsider the importance of slowing down in a world that demands go, go, go.

Click the above link to download the TED talk.

Fourteen attorneys general will challenge T-Mobile and Sprint merger in court this week

After months of statements, the biggest challenge yet to T-Mobile and Sprint’s proposed merger kicks off today in a Manhattan court. The trial is the result of pushback from a coalition of attorneys general of 13 states and the District of Columbia, who have raised flags over the proposed $26 billion merging of the country’s third- and fourth-largest carriers.

Texas joins growing list of AGs looking to block T-Mobile/Sprint merger

“Today we stand on the side of meaningful competition and affordable options for consumers,” California Attorney General Xavier Becerra said in a statement provided to TechCrunch. “Our airwaves belong to the public, who are entitled to more, not less. This merger would hurt the most vulnerable people among us– leaving consumers with fewer choices and higher prices. We’re fighting in court with a 14-state strong coalition for then, and for all Americans, and we’re confident the law is on our side.”

The AGs contend that such a merger will decrease competition in the U.S. telecom market, by knocking the number of major carriers down to three. T-Mobile and Sprint, on the other hand, have argued that it will do the opposite, suggesting that the companies’ pooled powers would better equip them to take on Verizon and AT&T in the rush to 5G.

FCC approves T-Mobile/Sprint merger despite serious concerns

Over the summer, FCC Chairman Ajit Pai issued an order essentially arguing with the carriers and suggested the deal move forward. “The evidence conclusively demonstrates that this transaction will bring fast 5G wireless service to many more Americans and help close the digital divide in rural areas,” he said in August.

The trial is expected to last three weeks, per The Wall Street Journal, kicking off with today’s opening statements. Sprint Chairman Marcelo Claure and soon-to-be-former T-Mobile CEO John Legere will take the stand to make their case against the AGs.

Read Full Article

Mindstrong Health hires leadership team with all-star tech product experience

Mindstrong Health is tackling one one of the most difficult challenges in healthcare: Severe mental illness, commonly referred to as SMI in the healthcare industry. The startup, founded in 2013 by Paul Dagum, Richard Klausner and Thomas Insel, recently brought on former Uber VP of Product Daniel Graf as CEO, and is now announcing a number of new hires to its senior product leadership team as it moves to turn the technology and research it’s developed over the past six years into an even more compelling and user-friendly product.

As mentioned, Graf was Mindstrong Health‘s first high-profile hire this year, when the former Uber, Twitter and Google product leader joined in October. Graf’s turn as the company’s chief executive is his return to the operational side after spending some time away from building product as an investor. He and Mindstrong have brought in four new c-suite execs to lead the company, including a new CPO, COO and CTO, as well as a new VP of Data Science and new VP of Marketing.

The CPO role is the only one Mindstrong can’t yet disclose, bu the incoming person has been a product leader at large tech companies, Graf told me in an interview. Meanwhile, the company is revealing that Brandon Trew (ex Uber, Google) will join as COO, Erik Albair (ex Google Maps, DeepMind) will join as CTO, Kane Sweeney (ex Uber, StubHub) comes in as VP of Data Science and Dena Olyaie (ex Facebook, Oscar Health) joins as VP of Marketing.

“The inflection point we’re going through now is really building out the whole foundation,” Graf told me. “If you look at our [current] app, it’s not an app, I would us as a consumer and from an experience point of view – it’s a science app. We basically have to build this whole foundation and platform, so for a technology person for a product person for a data scientist, for a marketing person, it’s kind of a dream. When you look at the planning stage, you look at the mission, with amazing investors, we don’t really have to worry about investments, and we can build this now. We can build this amazing platform and that’s why all these folks are joining.”

Mindstrong Health’s primary product is a platform that provides remote care on-demand for patients dealing with SMI. This group in particular faces challenges with current health care options, because they often face long wait times for appointments with qualified medical professionals, but their issues are pressing, hard to predict and often immediate in nature. Traditional care is also very expensive, and Mindstrong’s model has been shown to drive better results for patients, and to lower cost for insurance companies and other payers. Backed by ARCH Venture Partners, General Catalyst, Bezos Expeditions and more, the company has a number of ongoing trials with healthcare providers and patients, and based on the positive outcomes they’ve seen from this work the goal now is to refine and prepare the product for commercial use.

Graf’s new leadership team also shares a lot of experience building products that benefit from optimization based on interpretation of large data sets, and that’s also not a coincidence. Part of Mindstrong’s unique approach has been developing a way to quantify SMI issues in a way that makes it possible to anticipate problems based on signals from how a user is interacting with the app, including typing speed an other cues, as compared to an established personal baseline. It’s a big data problem, but instead of solving something like routing on-demand transportation, it’s tackling the issue of delivering reliable, quality care to individuals who are most in need.

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Google’s Pixels get a ‘feature drop’ with call screen and camera enhancements

Google this morning announced the arrival of its first “feature drop.” The new offering will continue the company’s regular feature enhancements, now arriving every month, like clockwork. This first one brings a whole bunch of upgrades, including a few already noted by some eagle eye views.

The call screen update is probably the biggest of the bunch. This one drops for Pixel 4 users in the U.S. to start, giving users screen unknown callers, filtering our robocalls in the process. When it’s not spam, users will get a notification shortly after featuring a transcript of the message. Google notes that all of that info is kept private to the the user, per the below gif. 

The Photos app gets a handy update, making it possible to add a background faux-bokeh blur to portrait photos, for the those times you forget to turn the feature on while shooting.

The Pixel 4 gets some key Duo improvements, as well, including auto framing, which keeps one or two people centered The feature appears to look similar to the more sophisticated versions found on the Nest Home Max (and Facebook’s Portal before it), zooming in and out to get people in frame.

Duo calls on the Pixel 2-4 also get a bokeh effect to blur out the background during calls, along with Smooth Display, which should offer better playback on spotty connections.

Also of note is the recently announced arrival of the extremely handy Recorder app on older Pixel models, along with the addition of Live Caption for the Pixel 3 and 3a. Users in UK, Canada, Ireland, Singapore and Australia, meanwhile, will be getting the updated version of Google Assistant soon, as well. 

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