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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 231–240 of 655 papers

TitleStatusHype
Efficient kernelized bandit algorithms via exploration distributions—0
Efficient Learning in Large-Scale Combinatorial Semi-Bandits—0
A Copula approach for hyperparameter transfer learning—0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
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