神刀安全网

ICML 2016 & Research at Google

Posted by Afshin Rostamizadeh, Research Scientist

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

转载本站任何文章请注明:转载至神刀安全网,谢谢神刀安全网 » ICML 2016 & Research at Google

分享到:更多 ()

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址