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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 281–290 of 655 papers

TitleStatusHype
Partial Likelihood Thompson Sampling—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Thompson Sampling with Unrestricted Delays—0
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
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
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