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Efficient Online Learning via Randomized Rounding

Efficient Online Learning via Randomized Rounding

This video was recorded at 25th Annual Conference on Neural Information Processing Systems (NIPS), Granada 2011. 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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