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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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