# PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

arXiv: https://arxiv.org/abs/1805.07458 · HTML: https://mlsanity.com/paper/1805.07458/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 19 of 19)

- [Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models](https://mlsanity.com/paper/2108.06422/) (2021)
- [Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits](https://mlsanity.com/paper/2205.13924/) (2022)
- [Regret Bounds for Generalized Linear Bandits under Parameter Drift](https://mlsanity.com/paper/2103.05750/) (2021)
- [An Experimental Design Approach for Regret Minimization in Logistic Bandits](https://mlsanity.com/paper/2202.02407/) (2022)
- [Thompson Sampling in Partially Observable Contextual Bandits](https://mlsanity.com/paper/2402.10289/) (2024)
- [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2112.11136/) (2021)
- [Computationally and Sample Efficient Safe Reinforcement Learning Using Adaptive Conformal Prediction](https://mlsanity.com/paper/2503.17678/) (2025)
- [Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification](https://mlsanity.com/paper/2109.14412/) (2021)
- [Stabilizing the Kumaraswamy Distribution](https://mlsanity.com/paper/2410.00660/) (2024)
- [Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits](https://mlsanity.com/paper/2203.04192/) (2022)
- [Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study](https://mlsanity.com/paper/2311.14359/) (2023)
- [Clustering Context in Off-Policy Evaluation](https://mlsanity.com/paper/2502.21304/) (2025)
- [Overcoming Prior Misspecification in Online Learning to Rank](https://mlsanity.com/paper/2301.10651/) (2023)
- [The Power of Active Multi-Task Learning in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2405.11226/) (2024)
- [Preference Learning with Response Time: Robust Losses and Guarantees](https://mlsanity.com/paper/2505.22820/) (2025)
- [BFTS: Thompson Sampling with Bayesian Additive Regression Trees](https://mlsanity.com/paper/2602.07767/) (2026)
- [Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype](https://mlsanity.com/paper/2505.16918/) (2025)
- [Odds-Ratio Thompson Sampling: A Specification and Design Guide for Contrast-Based Multi-Armed Bandits](https://mlsanity.com/paper/2609.19709/) (2026)
- [Risk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets](https://mlsanity.com/paper/2609.38914/) (2026)

## Abstract (arXiv)

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.