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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

Showing 251260 of 655 papers

TitleStatusHype
Mixed-Effect Thompson SamplingCode0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits0
Surrogate modeling for Bayesian optimization beyond a single Gaussian process0
Information-Directed Selection for Top-Two AlgorithmsCode0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization0
Non-Stationary Bandit Learning via Predictive Sampling0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
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