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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 651655 of 655 papers

TitleStatusHype
Combinatorial Neural Bandits0
Combining Bayesian Optimization and Lipschitz Optimization0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs0
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio0
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