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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 441–450 of 655 papers

TitleStatusHype
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial AttacksCode1
Efficient exploration of zero-sum stochastic games—0
On Thompson Sampling with Langevin Algorithms—0
Residual Bootstrap Exploration for Bandit Algorithms—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Thompson Sampling Algorithms for Mean-Variance BanditsCode0
Bayesian Quantile and Expectile Optimisation—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
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