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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 561–570 of 655 papers

TitleStatusHype
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
The Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Minimal Exploration in Structured Stochastic Bandits—0
Sequential Matrix Completion—0
A study of Thompson Sampling with Parameter h—0
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
Variational inference for the multi-armed contextual banditCode0
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