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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
Thompson Sampling for Pursuit-Evasion Problems—0
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
Thompson Sampling Algorithms for Cascading Bandits—0
Contextual Multi-Armed Bandits for Causal Marketing—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
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