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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 226–250 of 655 papers

TitleStatusHype
Efficient Exploration for LLMs—0
Efficient exploration of zero-sum stochastic games—0
Counterfactual Inference under Thompson Sampling—0
Efficient exploration with Double Uncertain Value Networks—0
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling—0
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
Efficient Learning in Large-Scale Combinatorial Semi-Bandits—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Asymptotically Optimal Bandits under Weighted Information—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Eluder Dimension and the Sample Complexity of Optimistic Exploration—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Ensemble Sampling—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Estimating prediction error for complex samples—0
Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits—0
Etat de l'art sur l'application des bandits multi-bras—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
Convolutional Monte Carlo Rollouts in Go—0
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