A Reliable Effective Terascale Linear Learning System
We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features (the number of features here refers to the number of non-zero entries in the data matrix), billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques is new, but the careful synthesis required to obtain an efficient implementation is a novel contribution. The result is, up to our knowledge, the most scalable and efficient linear learning system reported in the literature. We describe and thoroughly evaluate the components of the system, showing the importance of the various design choices.
Speaker Details
John Langford studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor’s degree in 1997, and received his Ph.D. from Carnegie Mellon University in 2002. Since then, he has worked at Yahoo!, Toyota Technological Institute, and IBM’s Watson Research Center. He is also the primary author of the popular Machine Learning weblog, hunch.net and the principle developer of Vowpal Wabbit. Previous research projects include Isomap, Captcha, Learning Reductions, Cover Trees, and Contextual Bandit learning.
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John Langford
Partner Researcher Manager
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Jeff Running
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