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NIPS 2015 and Machine Learning Research at Google
Sunday, December 6, 2015
Posted by Sanjiv Kumar, Research Scientist
This week, Montreal hosts the
29
th
Annual Conference on Neural Information Processing Systems
(NIPS 2015), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of some of the latest in machine learning research. Google will have a strong presence at NIPS 2015, with over 140 Googlers attending in order to contribute to and learn from the broader academic research community by presenting technical talks and posters, in addition to hosting workshops and tutorials.
Research at Google is at the forefront of innovation in Machine Intelligence, actively exploring virtually all aspects of
machine learning
including classical algorithms as well as cutting-edge techniques such as
deep learning
. Focusing on both theory as well as application, much of our work on language understanding, speech, translation, visual processing, ranking, and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, and develop learning approaches to understand and generalize.
If you are attending NIPS 2015, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at NIPS 2015 in the list below (Googlers highlighted in
blue
).
Google is a Platinum Sponsor of NIPS 2015.
PROGRAM ORGANIZERS
General Chairs
Corinna Cortes
, Neil D. Lawrence
Program Committee includes:
Samy Bengio
,
Gal Chechik
,
Ian Goodfellow
,
Shakir Mohamed
,
Ilya Sutskever
ORAL SESSIONS
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Vitaly Kuznetsov,
Mehryar Mohri
SPOTLIGHT SESSIONS
Distributed Submodular Cover: Succinctly Summarizing Massive Data
Baharan Mirzasoleiman, Amin Karbasi,
Ashwinkumar Badanidiyuru
, Andreas Krause
Spatial Transformer Networks
Max Jaderberg
,
Karen Simonyan
,
Andrew Zisserman
,
Koray Kavukcuoglu
Pointer Networks
Oriol Vinyals,
Meire Fortunato,
Navdeep Jaitly
Structured Transforms for Small-Footprint Deep Learning
Vikas Sindhwani
,
Tara Sainath
,
Sanjiv Kumar
Spherical Random Features for Polynomial Kernels
Jeffrey Pennington
,
Felix Yu
,
Sanjiv Kumar
POSTERS
Learning to Transduce with Unbounded Memory
Edward Grefenstette
,
Karl Moritz Hermann
,
Mustafa Suleyman,
Phil Blunsom
Deep Knowledge Tracing
Chris Piech, Jonathan Bassen,
Jonathan Huang,
Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein
Hidden Technical Debt in Machine Learning Systems
D Sculley,
Gary Holt
,
Daniel Golovin
,
Eugene Davydov
,
Todd Phillips
,
Dietmar Ebner
,
Vinay Chaudhary
,
Michael Young
,
Jean-Francois Crespo
,
Dan Dennison
Grammar as a Foreign Language
Oriol Vinyals
,
Lukasz Kaiser
,
Terry Koo
,
Slav Petrov
,
Ilya Sutskever
,
Geoffrey Hinton
Stochastic Variational Information Maximisation
Shakir Mohamed
,
Danilo Rezende
Embedding Inference for Structured Multilabel Prediction
Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Bing Xu,
Nan Ding
, Dale Schuurmans
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen,
Nan Ding
, Lawrence Carin
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
Rakesh Shivanna
, Bibaswan Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach
Differentially Private Learning of Structured Discrete Distributions
Ilias Diakonikolas,
Moritz Hardt
, Ludwig Schmidt
Nearly Optimal Private LASSO
Kunal Talwar
,
Li Zhang
, Abhradeep Thakurta
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess
,
Greg Wayne
,
David Silver
,
Timothy Lillicrap
,
Tom Erez
,
Yuval Tassa
Gradient Estimation Using Stochastic Computation Graphs
John Schulman
,
Nicolas Heess
,
Theophane Weber
, Pieter Abbeel
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio
,
Oriol Vinyals
,
Navdeep Jaitly
,
Noam Shazeer
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
,
Tomas Kocisky
,
Edward Grefenstette
,
Lasse Espeholt
,
Will Kay
,
Mustafa Suleyman
,
Phil Blunsom
Bayesian dark knowledge
Anoop Korattikara
,
Vivek Rathod
,
Kevin Murphy
, Max Welling
Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman,
Moritz Hardt
, Toniann Pitassi, Omer Reingold, Aaron Roth
Semi-supervised Sequence Learning
Andrew Dai
,
Quoc Le
Natural Neural Networks
Guillaume Desjardins
,
Karen Simonyan
,
Razvan Pascanu
,
Koray Kavukcuoglu
Revenue Optimization against Strategic Buyers
Andres Munoz Medina
,
Mehryar Mohri
WORKSHOPS
Feature Extraction: Modern Questions and Challenges
Workshop Chairs include:
Dmitry Storcheus
,
Afshin Rostamizadeh,
Sanjiv Kumar
Program Committee includes:
Jeffery Pennington
,
Vikas Sindhwani
NIPS Time Series Workshop
Invited Speakers include:
Mehryar Mohri
Panelists include:
Corinna Cortes
Nonparametric Methods for Large Scale Representation Learning
Invited Speakers include:
Amr Ahmed
Machine Learning for Spoken Language Understanding and Interaction
Invited Speakers include:
Larry Heck
Adaptive Data Analysis
Organizers include:
Moritz Hardt
Deep Reinforcement Learning
Organizers include :
David Silver
Invited Speakers include:
Sergey Levine
Advances in Approximate Bayesian Inference
Organizers include :
Shakir Mohamed
Panelists include:
Danilo Rezende
Cognitive Computation: Integrating Neural and Symbolic Approaches
Invited Speakers include:
Ramanathan V. Guha
,
Geoffrey Hinton
,
Greg Wayne
Transfer and Multi-Task Learning: Trends and New Perspectives
Invited Speakers include:
Mehryar Mohri
Poster presentations include:
Andres Munoz Medina
Learning and privacy with incomplete data and weak supervision
Organizers include :
Felix Yu
Program Committee includes:
Alexander Blocker
,
Krzysztof Choromanski
,
Sanjiv Kumar
Speakers include:
Nando de Freitas
Black Box Learning and Inference
Organizers include :
Ali Eslami
Keynotes include:
Geoff Hinton
Quantum Machine Learning
Invited Speakers include:
Hartmut Neven
Bayesian Nonparametrics: The Next Generation
Invited Speakers include:
Amr Ahmed
Bayesian Optimization: Scalability and Flexibility
Organizers include:
Nando de Freitas
Reasoning, Attention, Memory (RAM)
Invited speakers include:
Alex Graves
,
Ilya Sutskever
Extreme Classification 2015: Multi-class and Multi-label Learning in Extremely Large Label Spaces
Panelists include:
Mehryar Mohri
,
Samy Bengio
Invited speakers include:
Samy Bengio
Machine Learning Systems
Invited speakers include:
Jeff Dean
SYMPOSIA
Brains, Mind and Machines
Invited Speakers include:
Geoffrey Hinton
,
Demis Hassabis
Deep Learning Symposium
Program Committee Members include:
Samy Bengio
,
Phil Blunsom
,
Nando De Freitas
,
Ilya Sutskever
,
Andrew Zisserman
Invited Speakers include:
Max Jaderberg
,
Sergey Ioffe
,
Alexander Graves
Algorithms Among Us: The Societal Impacts of Machine Learning
Panelists include:
Shane Legg
TUTORIALS
NIPS 2015 Deep Learning Tutorial
Geoffrey E. Hinton
,
Yoshua Bengio
,
Yann LeCun
Large-Scale Distributed Systems for Training Neural Networks
Jeff Dean
,
Oriol Vinyals
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