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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.

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Showing 276–300 of 655 papers

TitleStatusHype
Generalized Bayesian deep reinforcement learning—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors—0
Generalized Thompson Sampling for Contextual Bandits—0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions—0
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits—0
Graph Neural Thompson Sampling—0
Feedback graph regret bounds for Thompson Sampling and UCB—0
Greedy Bandits with Sampled Context—0
Greedy k-Center from Noisy Distance Samples—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
Hierarchical Bayesian Bandits—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Horde of Bandits using Gaussian Markov Random Fields—0
Human collective intelligence as distributed Bayesian inference—0
Hypermodels for Exploration—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
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