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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 431–440 of 655 papers

TitleStatusHype
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
Stable Thompson Sampling: Valid Inference via Variance Inflation—0
Stage-wise Conservative Linear Bandits—0
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits—0
Stochastically Constrained Best Arm Identification with Thompson Sampling—0
Stochastic Neural Network with Kronecker Flow—0
Streaming kernel regression with provably adaptive mean, variance, and regularization—0
Surrogate modeling for Bayesian optimization beyond a single Gaussian process—0
Synthetically Controlled Bandits—0
Taming Non-stationary Bandits: A Bayesian Approach—0
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