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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 121–130 of 655 papers

TitleStatusHype
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits—0
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Tree Ensembles for Contextual Bandits—0
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Efficient Exploration for LLMs—0
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis—0
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