Efficient Transductive Online Learning via Randomized Rounding

Nicolò Cesa-Bianchi; Ohad Shamir

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arXiv · 1106.2429

Efficient Transductive Online Learning via Randomized Rounding

Nicolò Cesa-Bianchi·Ohad Shamir

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Abstract

Most traditional online learning algorithms 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, tailored for transductive settings, which combines "random playout" and randomized rounding of loss subgradients. As an application of our approach, we present 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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Nicolò Cesa-Bianchi, Ohad Shamir. 2013-09-11. Efficient Transductive Online Learning via Randomized Rounding. https://arxiv.org/abs/1106.2429

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