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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