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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 471–480 of 655 papers

TitleStatusHype
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
On Online Learning in Kernelized Markov Decision Processes—0
On The Differential Privacy of Thompson Sampling With Gaussian Prior—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
On the Performance of Thompson Sampling on Logistic Bandits—0
On the Prior Sensitivity of Thompson Sampling—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
On Thompson Sampling with Langevin Algorithms—0
On Frequentist Regret of Linear Thompson Sampling—0
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