Google Research Blog
The latest news from Research at Google
NIPS 2016 & Research at Google
Sunday, December 04, 2016
Posted by Doug Eck, Research Scientist, Google Brain Team
This week, Barcelona hosts the
30
th
Annual Conference on Neural Information Processing Systems
(NIPS 2016), 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 2016, with over 280 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 2016, 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, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented at NIPS 2016 in the list below (Googlers highlighted in
blue
).
Google is a Platinum Sponsor of NIPS 2016.
Organizing Committee
Executive Board includes:
Corinna Cortes, Fernando Pereira
Advisory Board includes:
John C. Platt
Area Chairs include:
John Shlens
,
Moritz Hardt
,
Navdeep Jaitly
,
Hugo Larochelle
,
Honglak Lee
,
Sanjiv Kumar
,
Gal Chechik
Invited Talk
Dynamic Legged Robots
Marc Raibert
Accepted Papers:
Boosting with Abstention
Corinna Cortes
, Giulia DeSalvo,
Mehryar Mohri
Community Detection on Evolving Graphs
Stefano Leonardi, Aris Anagnostopoulos, Jakub Łącki,
Silvio Lattanzi
,
Mohammad Mahdian
Linear Relaxations for Finding Diverse Elements in Metric Spaces
Aditya Bhaskara, Mehrdad Ghadiri,
Vahab Mirrokni
, Ola Svensson
Nearly Isometric Embedding by Relaxation
James McQueen, Marina Meila,
Dominique Joncas
Optimistic Bandit Convex Optimization
Mehryar Mohri
, Scott Yang
Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi
,
Samy Bengio
,
Zhifeng Chen
,
Navdeep Jaitly
,
Mike Schuster
,
Yonghui Wu
,
Dale Schuurmans
Stochastic Gradient MCMC with Stale Gradients
Changyou Chen,
Nan Ding
, Chunyuan Li, Yizhe Zhang, Lawrence Carin
Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn
*
, Ian Goodfellow,
Sergey Levine
Using Fast Weights to Attend to the Recent Past
Jimmy Ba,
Geoffrey Hinton
, Volodymyr Mnih, Joel Leibo, Catalin Ionescu
A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang, He He,
Stephane Ross
, Hal III
A Neural Transducer
Navdeep Jaitly
,
Quoc Le
, Oriol Vinyals, Ilya Sutskever,
David Sussillo
,
Samy Bengio
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu,
Geoffrey Hinton
Bi-Objective Online Matching and Submodular Allocations
Hossein Esfandiari,
Nitish Korula
,
Vahab Mirrokni
Combinatorial Energy Learning for Image Segmentation
Jeremy Maitin-Shepard
,
Viren Jain
,
Michal Januszewski
,
Peter Li
, Pieter Abbeel
Deep Learning Games
Dale Schuurmans
,
Martin Zinkevich
DeepMath - Deep Sequence Models for Premise Selection
Geoffrey Irving
,
Christian Szegedy
,
Niklas Een
,
Alexander Alemi
,
François Chollet
, Josef Urban
Density Estimation via Discrepancy Based Adaptive Sequential Partition
Dangna Li,
Kun Yang
, Wing Wong
Domain Separation Networks
Konstantinos Bousmalis
, George Trigeorgis,
Nathan Silberman
,
Dilip Krishnan
,
Dumitru Erhan
Fast Distributed Submodular Cover: Public-Private Data Summarization
Baharan Mirzasoleiman,
Morteza Zadimoghaddam
, Amin Karbasi
Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh,
Andrew Cotter
,
Maya Gupta
, Michael P Friedlander
Can Active Memory Replace Attention?
Łukasz Kaiser
,
Samy Bengio
Fast and Flexible Monotonic Functions with Ensembles of Lattices
Kevin Canini
,
Andy Cotter
,
Maya Gupta
,
Mahdi Fard
,
Jan Pfeifer
Launch and Iterate: Reducing Prediction Churn
Quentin Cormier,
Mahdi Fard, Kevin Canini, Maya Gupta
On Mixtures of Markov Chains
Rishi Gupta,
Ravi Kumar
,
Sergei Vassilvitskii
Orthogonal Random Features
Felix Xinnan Yu
,
Ananda Theertha Suresh
,
Krzysztof Choromanski
,
Dan Holtmann-Rice
,
Sanjiv Kumar
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D
Supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo,
Honglak Lee
Structured Prediction Theory Based on Factor Graph Complexity
Corinna Cortes
,
Vitaly Kuznetsov
,
Mehryar Mohri
, Scott Yang
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely
,
Roy Frostig
,
Yoram Singer
Demonstrations
Interactive musical improvisation with Magenta
Adam Roberts
,
Sageev Oore
,
Curtis Hawthorne
,
Douglas Eck
Content-based Related Video Recommendation
Joonseok Lee
Workshops, Tutorials and Symposia
Advances in Approximate Bayesian Inference
Advisory Committee includes:
Kevin P. Murphy
Invited Speakers include:
Matt Johnson
Panelists include:
Ryan Sepassi
Adversarial Training
Accepted Authors:
Luke Metz
,
Ben Poole
,
David Pfau
,
Jascha Sohl-Dickstein
,
Augustus Odena
,
Christopher Olah
,
Jonathon Shlens
Bayesian Deep Learning
Organizers include:
Kevin P. Murphy
Accepted Authors include:
Rif A. Saurous
,
Eugene Brevdo
,
Kevin Murphy
,
Eric Jang
,
Shixiang Gu
,
Ben Poole
Brains & Bits: Neuroscience Meets Machine Learning
Organizers include:
Jascha Sohl-Dickstein
Connectomics II: Opportunities & Challanges for Machine Learning
Organizers include:
Viren Jain
Constructive Machine Learning
Invited Speakers include:
Douglas Eck
Continual Learning & Deep Networks
Invited Speakers include:
Honglak Lee
Deep Learning for Action & Interaction
Organizers include:
Sergey Levine
Invited Speakers include:
Honglak Lee
Accepted Authors include:
Pararth Shah
,
Dilek Hakkani-Tur
,
Larry Heck
End-to-end Learning for Speech and Audio Processing
Invited Speakers include:
Tara Sainath
Accepted Authors include:
Brian Patton
,
Yannis Agiomyrgiannakis
,
Michael Terry
,
Kevin Wilson
,
Rif A. Saurous
,
D. Sculley
Extreme Classification: Multi-class & Multi-label Learning in Extremely Large Label Spaces
Organizers include:
Samy Bengio
Interpretable Machine Learning for Complex Systems
Invited Speaker:
Honglak Lee
Accepted Authors include:
Daniel Smilkov
,
Nikhil Thorat
,
Charles Nicholson
,
Emily Reif
,
Fernanda Viegas
,
Martin Wattenberg
Large Scale Computer Vision Systems
Organizers include:
Gal Chechik
Machine Learning Systems
Invited Speakers include:
Jeff Dean
Nonconvex Optimization for Machine Learning: Theory & Practice
Organizers include:
Hossein Mobahi
Optimizing the Optimizers
Organizers include:
Alex Davies
Reliable Machine Learning in the Wild
Accepted Authors:
Andres Medina
,
Sergei Vassilvitskii
The Future of Gradient-Based Machine Learning Software
Invited Speakers:
Jeff Dean
,
Matt Johnson
Time Series Workshop
Organizers include:
Vitaly Kuznetsov
Invited Speakers include:
Mehryar Mohri
Theory and Algorithms for Forecasting Non-Stationary Time Series
Tutorial Organizers:
Vitaly Kuznetsov,
Mehryar Mohri
Women in Machine Learning
Invited Speakers include:
Maya Gupta
*
Work done as part of the Google Brain team
↩
NIPS 2015 and Machine Learning Research at Google
Sunday, December 06, 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
Advances in Variational Inference: Working Towards Large-scale Probabilistic Machine Learning at NIPS 2014
Monday, December 01, 2014
Posted by Shakir Mohamed and Charles Blundell, Google DeepMind, London
At Google, we continually explore and develop large-scale machine learning systems to improve our user’s experience, such as providing better video recommendations, deciding on the best language translation in a given context, or improving the accuracy of image search results. The data used to train these systems often contains many inconsistencies and missing elements, making progress towards large-scale probabilistic models designed to address these problems an important and ongoing part of our research. One principled and efficient approach for developing such models relies on an approach known as
Variational Inference
.
A renewed interest and several recent advances in variational inference
1,2,3,4,5,6
has motivated us to support and co-organise this year’s workshop on
Advances in Variational Inference
as part of the
Neural Information Processing Systems
(NIPS) conference in Montreal. These advances include new methods for scalability using
stochastic gradient methods
, the ability to handle data that arrives continuously as a
stream
, inference in non-linear
time-series models
, principled regularisation in
deep neural networks
, and inference-based decision making in
reinforcement learning
, amongst others.
Whilst variational methods have clearly emerged as a leading approach for tractable, large-scale probabilistic inference, there remain important trade-offs in speed, accuracy, simplicity and applicability between variational and other approximative schemes. The goal of the workshop will be to contextualise these developments and address some of the many unanswered questions through:
Contributed talks from 6 speakers
who are leading the resurgence of variational inference, and shaping the debate on topics of stochastic optimisation, deep learning, Bayesian non-parametrics, and theory.
34 contributed papers
covering significant advances in methodology, theory and applications including efficient optimisation, streaming data analysis, submodularity, non-parametric modelling and message passing.
A panel discussion with leading researchers
in the field that will further interrogate these ideas. Our panelists are
David Blei
,
Neil Lawrence
,
Shinichi Nakajima
and
Matthias Seeger
.
The workshop presents a fantastic opportunity to discuss the opportunities and obstacles facing the wider adoption of variational methods. The workshop will be held on the 13th December 2014 at the Montreal Convention and Exhibition Centre. For more details see:
www.variationalinference.org
.
References:
1.
Rezende, Danilo J., Shakir Mohamed, and Daan Wierstra
,
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
,
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.
2.
Gregor, Karol, Ivo Danihelka, Andriy Mnih, Charles Blundell and Daan Wierstra
,
Deep AutoRegressive Networks
,
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.
3.
Mnih, Andriy, and Karol Gregor,
Neural Variational Inference and Learning in Belief Networks
, Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014.
4.
Kingma, D. P. and Welling, M.,
Auto-Encoding Variational Bayes
, Proceedings of the International Conference on Learning Representations (ICLR), 2014.
5.
Broderick, T., Boyd, N., Wibisono, A., Wilson, A. C., & Jordan, M.
,
Streaming Variational Bayes
,
Advances in Neural Information Processing Systems (pp. 1727-1735), 2013.
6.
Hoffman, M., Blei, D. M., Wang, C., and Paisley, J.,
Stochastic Variational Inference
, Journal of Machine Learning Research, 14:1303–1347, 2013.
Announcing Google-hosted workshop videos from NIPS 2011
Thursday, February 23, 2012
Posted by John Blitzer and Douglas Eck, Google Research
At the
25th Neural Information Processing Systems (NIPS)
conference in Granada, Spain last December, we engaged in dialogue with a diverse population of neuroscientists, cognitive scientists, statistical learning theorists, and machine learning researchers. More than twenty Googlers participated in an intensive single-track program of talks, nightly poster sessions and a workshop weekend in the Spanish Sierra Nevada mountains. Check out the
NIPS 2011 blog post
for full information on Google at NIPS.
In conjunction with our technical involvement and gold sponsorship of NIPS, we recorded the five workshops that Googlers helped to organize on various topics from big learning to music. We’re now pleased to provide access to these rich workshop experiences to the wider technical community.
Watch videos of Googler-led workshops on the
YouTube Tech Talks Channel
:
Big Learning: Algorithms, Systems, and Tools for Learning at Scale
by Joseph Gonzalez, Sameer Singh, Graham Taylor, James Bergstra, Alice Zheng, Misha Bilenko, Yucheng Low, Yoshua Bengio, Michael Franklin, Carlos Guestrin, Andrew McCallum, Alexander Smola, Michael Jordan, Sugato Basu (Googler)
Domain Adaptation Workshop: Theory and Application
by John Blitzer, Corinna Cortes, Afshin Rostamizadeh (all Googlers)
Learning Semantics
by Antoine Bordes, Jason Weston (Googler), Ronan Collobert, Leon Bottou
Sparse Representation and Low-rank Approximation
by Ameet Talwalkar, Lester Mackey, Mehryar Mohri (Googler), Michael Mahoney, Francis Bach, Mike Davies, Remi Gribonval, Guillaume Obozinski
International Workshop on Music and Machine Learning: Learning from Musical Structure
by Rafael Ramirez, Darrell Conklin, Douglas Eck (Googler), Ryan Rifkin (Googler)
To highlight a few workshops:
The Domain Adaptation
workshop organized by Google, which fused theoretical and practical domain adaptation, featured invited talks from Shai Ben-David and Googler Mehryar Mohri from the theory side and Dan Roth from the applications side. This was just next door to Googlers Doug Eck and Ryan Rifkin's workshop on
Machine Learning and Music
, with musical demonstrations loud enough for the next-door neighbors to ask them to “turn it down a bit, please.” In addition to the Googler-run workshops, the
Integrating Language and Vision
workshop showcased invited talks by Google postdoctoral fellow Percy Liang on the pragmatics of visual scene description and Josh Tenenbaum on physical models as a cognitive plausible mechanism for bridging language and vision. Finally, Google consultant Andrew Ng was one of the organizers of the
Deep Learning and Unsupervised Feature Learning
, which offered an extended tutorial, several inspiring talks, and two panel discussions (one with Googler Samy Bengio as panelist) exploring the question of “How deep is deep?”
As the workshop weekend drew to a close, an airline strike in Spain left NIPS attendees scrambling to get home for the holidays. We hope the skies look clear for 2012 when NIPS lands in Google’s neck of the woods, Lake Tahoe!
Google at NIPS 2010
Thursday, January 27, 2011
Posted by Slav Petrov, Doug Aberdeen, and Lisa McCracken, Google Research
The machine learning community met in Vancouver in December for the 24th
Neural Information Processing Systems Conference (NIPS)
. As always, the single-track program of the main conference featured a number of outstanding talks, followed by interesting late night poster sessions. A record number of workshops covered a wide variety of topics, while allocating sufficient time for skiing in Whistler - after all, many of the most interesting research conversations happen while riding the lift in-between ski runs. This year’s conference also featured a symposium dedicated to
Sam Roweis
, providing a retrospective on Sam’s life and work. Sam, a fellow Googler and professor at NYU, was at the heart of the NIPS community and is terribly missed.
As always, Google was involved in various ways with NIPS. Here at Google, we take a data-driven approach when solving problems. Therefore, Machine Learning is in one way or another at the core of most of the things that we do. It is therefore unsurprising that many Googlers helped shape the program of the conference or were in the audience. This year, three Googlers served as area chairs and even more were reviewers. Googlers also co-authored the following papers:
Label Embedding Trees for Large Multi-Class Tasks
by Samy Bengio and Jason Weston
Learning Bounds for Importance Weighting
by Corinna Cortes, Yishay Mansour, and Mehryar Mohri
Online Learning in the Manifold of Low-Rank Matrices
by Uri Shalit, Daphna Weinshall, and Gal Chechik
Deterministic Single–Pass Algorithm for LDA
by Issei Sato, Kenichi Kurihara, and Hiroshi Nakagawa
Distributed Dual Averaging In Networks
by John Duchi, Alekh Agarwal, and Martin Wainwright
Additionally, Googlers co-organized three well attended workshops:
Coarse–to–Fine Learning and Inference
by Ben Taskar, David Weiss, Benjamin Sapp, and Slav Petrov
Low–rank Methods for Large–scale Machine Learning
by Arthur Gretton, Michael Mahoney, Mehryar Mohri, and Ameet Talwalkar
Learning on Cores, Clusters, and Clouds
by John Duchi, Ofer Dekel, John Langford, Lawrence Cayton, and Alekh Agarwal
Finally, Yoram Singer gave a great talk on
Learning Structural Sparsity
at the Sam Roweis symposium and Googlers presented the following talks during the workshops:
Online Learning in the Manifold of Low–Rank Matrices
by Uri Shalit, Daphna Weinshall, and Gal Chechik
Distributed MAP Inference for Undirected Graphical Models
by Sameer Singh, Amar Subramanya, Fernando Pereira, and Andrew McCallum
MapReduce/Bigtable for Distributed Optimization
by Keith Hall, Scott Gilpin and Gideon Mann
Self-Pruning Prediction Trees
by Sally Goldman
Web Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings
by Jason Weston, Samy Bengio, and Nicolas Usunier
Coarse–to–fine Decoding for Parsing and Machine Translation
by Slav Petrov
Overall, it was a very successful conference and it was good to be back in Vancouver one last time. This coming year
NIPS 2011
will be in Granada, Spain. Hasta luego!
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