Efficient Online Learning via Randomized Rounding

author: Ohad Shamir, Faculty of Mathematics and Computer Science, Weizmann Institute of Science
published: Jan. 25, 2012,   recorded: December 2011,   views: 149
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Description

Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, which combines "random playout" and randomized rounding of loss subgradients. As an application of our approach, we provide the first computationally efficient online algorithm for collaborative filtering with trace-norm constrained matrices. As a second application, we solve an open question linking batch learning and transductive online learning.

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