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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.

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Showing 451–460 of 655 papers

TitleStatusHype
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
Thompson Sampling with Approximate Inference—0
Thompson Sampling and Approximate Inference—0
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for 1-Dimensional Exponential Family Bandits—0
Thompson Sampling for Adversarial Bit Prediction—0
Thompson Sampling for Bandits with Clustered Arms—0
Thompson Sampling for Budgeted Multi-armed Bandits—0
Thompson Sampling Algorithms for Cascading Bandits—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
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