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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 571–580 of 655 papers

TitleStatusHype
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
An Empirical Evaluation of Thompson Sampling—0
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders—0
An improved regret analysis for UCB-N and TS-N—0
An Information-Theoretic Analysis for Thompson Sampling with Many Actions—0
An Information-Theoretic Analysis of Thompson Sampling—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles—0
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