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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 521–530 of 655 papers

TitleStatusHype
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation MethodsCode0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Combining Bayesian Optimization and Lipschitz Optimization—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Thompson Sampling Algorithms for Cascading Bandits—0
Efficient Linear Bandits through Matrix Sketching—0
Incorporating Behavioral Constraints in Online AI Systems—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling—0
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