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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 421–430 of 655 papers

TitleStatusHype
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
On Frequentist Regret of Linear Thompson Sampling—0
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits—0
Scalable Thompson Sampling using Sparse Gaussian Process Models—0
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start UsersCode1
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
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