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Oral
Efficient Online Learning via Randomized Rounding
Nicolò Cesa-Bianchi · Ohad Shamir
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.
Author Information
Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy)
Ohad Shamir (Weizmann Institute of Science)
Related Events (a corresponding poster, oral, or spotlight)
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2011 Poster: Efficient Online Learning via Randomized Rounding »
Wed. Dec 14th 04:45 -- 10:59 PM Room
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