This week, New York hosts the 2016 International Conference on Machine Learning (ICML 2016), a premier annual Machine Learning event supported by the International Machine Learning Society (IMLS). Machine Learning is a key focus area at Google, with highly active research groups exploring virtually all aspects of the field, including deep learning and more classical algorithms.
We work on an extremely wide variety of machine learning problems that arise from a broad range of applications at Google. One particularly important setting is that of large-scale learning, where we utilize scalable tools and architectures to build machine learning systems that work with large volumes of data that often preclude the use of standard single-machine training algorithms. In doing so, we are able to solve deep scientific problems and engineering challenges, exploring theory as well as application, in areas of language, speech, translation, music, visual processing and more.
As Gold Sponsor, Google has a strong presence at ICML 2016 with many Googlers publishing their research and hosting workshops. If you’re attending, 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 interesting ML problems that impact millions of people. You can also learn more about our research being presented at ICML 2016 in the list below (Googlers highlighted in blue).
ICML 2016 Organizing Committee
Area Chairs include: Corinna Cortes, John Blitzer, Maya Gupta, Moritz Hardt, Samy Bengio
IMLS
Board Members include: Corinna Cortes
Accepted Papers
ADIOS: Architectures Deep In Output Space
Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio
Associative Long Short-Term Memory
Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
Binary embeddings with structured hashed projections
Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun
Discrete Distribution Estimation Under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Nando de Freitas, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu
Fast Constrained Submodular Maximization: Personalized Data Summarization
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine
Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
One-Shot Generalization in Deep Generative Models
Danilo Rezende, Shakir Mohamed, Daan Wierstra
Pixel Recurrent Neural Networks
Aaron Van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
Pricing a low-regret seller
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
Primal-Dual Rates and Certificates
Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Thorsten Joachims, Adith Swaminathan, Ashudeep Singh, Navin Chandak
Recycling Randomness with Structure for Sublinear Time Kernel Expansions
Krzysztof Choromanski, Vikas Sindhwani
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, Yoram Singer
Variational Inference for Monte Carlo Objectives
Andriy Mnih, Danilo Rezende
Workshops
Abstraction in Reinforcement Learning
Organizing Committee: Daniel Mankowitz, Timothy Mann, Shie Mannor
Invited Speaker: David Silver
Deep Learning Workshop
Organizers: Antoine Bordes, Kyunghyun Cho, Emily Denton, Nando de Freitas, Rob Fergus
Invited Speaker: Raia Hadsell
Neural Networks Back To The Future
Organizers: Léon Bottou, David Grangier, Tomas Mikolov, John Platt
Data-Efficient Machine Learning
Organizers: Marc Deisenroth, Shakir Mohamed, Finale Doshi-Velez, Andreas Krause, Max Welling
On-Device Intelligence
Organizers: Vikas Sindhwani, Daniel Ramage, Keith Bonawitz, Suyog Gupta, Sachin Talathi
Invited Speakers: Hartwig Adam, H. Brendan McMahan
Online Advertising Systems
Organizing Committee: Sharat Chikkerur, Hossein Azari, Edoardo Airoldi
Opening Remarks: Hossein Azari
Invited Speakers: Martin Pál, Todd Phillips
Tutorials
Deep Reinforcement Learning
David Silver
Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis
Moritz Hardt, Aaron Roth
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