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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 221–230 of 655 papers

TitleStatusHype
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
A Copula approach for hyperparameter transfer learning—0
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Efficient Exploration for LLMs—0
Efficient exploration of zero-sum stochastic games—0
Bandits Under The Influence (Extended Version)—0
Efficient exploration with Double Uncertain Value Networks—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
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