  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.
