Machine learning (ML) 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 ML research, Google is proud to be a Platinum Sponsor of the thirty-fourth International Conference on Machine Learning (ICML 2017), a premier annual event supported by the International Machine Learning Society taking place this week in Sydney, Australia. With over 130 Googlers attending the conference to present publications and host workshops, we look forward to our continued collaboration with the larger ML research community.
If you're attending ICML 2017, we hope you'll visit the Google booth and talk with our researchers to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges. Our researchers will also be available to talk about and demo several recent efforts, including the technology behind Facets, neural audio synthesis with Nsynth, a Q&A session on the Google Brain Residency program and much more. You can also learn more about our research being presented at ICML 2017 in the list below (Googlers highlighted in blue).
ICML 2017 Committees
Senior Program Committee includes: Alex Kulesza, Amr Ahmed, Andrew Dai, Corinna Cortes, George Dahl, Hugo Larochelle, Matthew Hoffman, Maya Gupta, Moritz Hardt, Quoc Le
Sponsorship Co-Chair: Ryan Adams
Publications
Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta
Tight Bounds for Approximate Carathéodory and Beyond
Vahab Mirrokni, Renato Leme, Adrian Vladu, Sam Wong
Sharp Minima Can Generalize For Deep Nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio
Geometry of Neural Network Loss Surfaces via Random Matrix Theory
Jeffrey Pennington, Yasaman Bahri
Conditional Image Synthesis with Auxiliary Classifier GANs
Augustus Odena, Christopher Olah, Jon Shlens
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Matthew D. Hoffman
On the Expressive Power of Deep Neural Networks
Maithra Raghu, Ben Poole, Surya Ganguli, Jon Kleinberg, Jascha Sohl-Dickstein
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew Hoffman, Sergio Gomez, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Satyen Kale, Zohar Karnin, Tengyuan Liang, David Pal
Algorithms for ℓp Low-Rank Approximation
Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David Woodruff
Consistent k-Clustering
Silvio Lattanzi, Sergei Vassilvitskii
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Jakob Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Colin Raffel, Thang Luong, Peter Liu, Ron Weiss, Douglas Eck
Gradient Boosted Decision Trees for High Dimensional Sparse Output
Si Si, Huan Zhang, Sathiya Keerthi, Dhruv Mahajan, Inderjit Dhillon, Cho-Jui Hsieh
Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, Jose Hernandez-Lobato, Richard E Turner, Douglas Eck
Uniform Convergence Rates for Kernel Density Estimation
Heinrich Jiang
Density Level Set Estimation on Manifolds with DBSCAN
Heinrich Jiang
Maximum Selection and Ranking under Noisy Comparisons
Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Suresh
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Cinjon Resnick, Adam Roberts, Jesse Engel, Douglas Eck, Sander Dieleman, Karen Simonyan, Mohammad Norouzi
Distributed Mean Estimation with Limited Communication
Ananda Suresh, Felix Yu, Sanjiv Kumar, Brendan McMahan
Learning to Generate Long-term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew Miller, Nicholas J Foti, Ryan Adams
RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Suresh
Axiomatic Attribution for Deep Networks
Ankur Taly, Qiqi Yan,,Mukund Sundararajan
Differentiable Programs with Neural Libraries
Alex L Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Manzil Zaheer, Amr Ahmed, Alex Smola
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc Le, Benoit Steiner, Mohammad Norouzi, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Samy Bengio, Jeff Dean
Canopy — Fast Sampling with Cover Trees
Manzil Zaheer, Satwik Kottur, Amr Ahmed, Jose Moura, Alex Smola
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli
Probabilistic Submodular Maximization in Sub-Linear Time
Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Michael Gygli, Mohammad Norouzi, Anelia Angelova
Stochastic Generative Hashing
Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Jonathan Tompson, Kristofer D Schlachter, Pablo Sprechmann, Ken Perlin
Large-Scale Evolution of Image Classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alexey Kurakin
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel Schoenholz, Patrick Riley, Oriol Vinyals, George Dahl
Neural Optimizer Search with Reinforcement Learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc Le
Workshops
Implicit Generative Models
Organizers include: Ian Goodfellow
Learning to Generate Natural Language
Accepted Papers include:
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, Ray Kurzweil
Lifelong Learning: A Reinforcement Learning Approach
Accepted Papers include:
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans
Principled Approaches to Deep Learning
Organizers include: Robert Gens
Program Committee includes: Jascha Sohl-Dickstein
Workshop on Human Interpretability in Machine Learning (WHI)
Organizers include: Been Kim
ICML Workshop on TinyML: ML on a Test-time Budget for IoT, Mobiles, and Other Applications
Invited speakers include: Sujith Ravi
Deep Structured Prediction
Organizers include: Gal Chechik, Ofer Meshi
Program Committee includes: Vitaly Kuznetsov, Kevin Murphy
Invited Speakers include: Ryan Adams
Accepted Papers include:
Filtering Variational Objectives
Chris J Maddison, Dieterich Lawson, George Tucker, Mohammad Norouzi, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
Machine Learning in Speech and Language Processing
Organizers include: Tara Sainath
Invited speakers include: Ron Weiss
Picky Learners: Choosing Alternative Ways to Process Data
Invited speakers include: Tomer Koren
Organizers include: Corinna Cortes, Mehryar Mohri
Private and Secure Machine Learning
Keynote Speakers include: Ilya Mironov
Reproducibility in Machine Learning Research
Invited Speakers include: Hugo Larochelle, Francois Chollet
Organizers include: Samy Bengio
Time Series Workshop
Organizers include: Vitaly Kuznetsov
Tutorial
Interpretable Machine Learning
Presenters include: Been Kim
