Thompson Sampling for Online Learning with Linear Experts

Aditya Gopalan

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arXiv · 1311.0468

Thompson Sampling for Online Learning with Linear Experts

Aditya Gopalan

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Abstract

In this note, we present a version of the Thompson sampling algorithm for the problem of online linear generalization with full information (i.e., the experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a Gaussian prior and time-varying Gaussian likelihoods, and we show that it essentially reduces to Kalai and Vempala's Follow-the-Perturbed-Leader strategy, with exponentially distributed noise replaced by Gaussian noise. This implies sqrt(T) regret bounds for Thompson sampling (with time-varying likelihood) for online learning with full information.

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BibTeX RIS

Aditya Gopalan. 2013-11-03. Thompson Sampling for Online Learning with Linear Experts. https://arxiv.org/abs/1311.0468

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