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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 341–350 of 655 papers

TitleStatusHype
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
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment—0
Optimal Exploration is no harder than Thompson Sampling—0
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