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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 481–490 of 655 papers

TitleStatusHype
Scaling Multi-Armed Bandit Algorithms—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop PlanningCode0
Thompson Sampling on Symmetric α-Stable Bandits—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
Mixed-Variable Bayesian Optimization—0
Bandit Learning for Diversified Interactive Recommendation—0
Thompson Sampling for Adversarial Bit Prediction—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Sparse Spectrum Gaussian Process for Bayesian Optimization—0
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