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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 271–280 of 655 papers

TitleStatusHype
Double Thompson Sampling in Finite stochastic Games—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Synthetically Controlled Bandits—0
Remote Contextual Bandits—0
Fourier Representations for Black-Box Optimization over Categorical Variables—0
On learning Whittle index policy for restless bandits with scalable regret—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Tsetlin Machine for Solving Contextual Bandit ProblemsCode0
Deep Hierarchy in Bandits—0
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