PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

Bianca Dumitrascu; Karen Feng; Barbara E Engelhardt

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arXiv · 1805.07458

PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

Bianca Dumitrascu·Karen Feng·Barbara E Engelhardt

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Abstract

We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards. Using a fast inference procedure with Polya-Gamma distributed augmentation variables, we propose an improved version of Thompson Sampling, a Bayesian formulation of contextual bandits with near-optimal performance. Our approach, Polya-Gamma augmented Thompson Sampling (PG-TS), achieves state-of-the-art performance on simulated and real data. PG-TS explores the action space efficiently and exploits high-reward arms, quickly converging to solutions of low regret. Its explicit estimation of the posterior distribution of the context feature covariance leads to substantial empirical gains over approximate approaches. PG-TS is the first approach to demonstrate the benefits of Polya-Gamma augmentation in bandits and to propose an efficient Gibbs sampler for approximating the analytically unsolvable integral of logistic contextual bandits.

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Bianca Dumitrascu, Karen Feng, Barbara E Engelhardt. 2018-05-18. PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits. https://arxiv.org/abs/1805.07458

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