Large-Scale Machine Learning: Parallelism and Massive Datasets
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin

Fri Dec 11th 07:30 AM -- 06:30 PM @ Hilton: Mt. Currie North
Event URL: »

Physical and economic limitations have forced computer architecture towards parallelism and away from exponential frequency scaling. Meanwhile, increased access to ubiquitous sensing and the web has resulted in an explosion in the size of machine learning tasks. In order to benefit from current and future trends in processor technology we must discover, understand, and exploit the available parallelism in machine learning. This workshop will achieve four key goals:

*Bring together people with varying approaches to parallelism in machine learning to identify tools, techniques, and algorithmic ideas which have lead to successful parallel learning.

*Invite researchers from related fields, including parallel algorithms, computer architecture, scientific computing, and distributed systems, who will provide new perspectives to the NIPS community on these problems, and may also benefit from future collaborations with the NIPS audience.

*Identify the next key challenges and opportunities to parallel learning.

*Discuss large-scale applications, e.g., those with real time demands, that might benefit from parallel learning.

Prior NIPS workshops have focused on the topic of scaling machine learning, which remains an important developing area. We introduce a new perspective by focusing on how large-scale machine learning algorithms should be informed by future parallel architectures.

Author Information

Alexander Gray (Skytree Inc. and Georgia Tech)
Arthur Gretton (Gatsby Unit, UCL)

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).

Alex Smola (Amazon - We are hiring!)

**Amazon AWS Machine Learning** We are hiring!

Joseph E Gonzalez (Carnegie Mellon University)
Carlos Guestrin (University of Washington)

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