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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 201–225 of 655 papers

TitleStatusHype
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Trajectory-oriented optimization of stochastic epidemiological modelsCode0
An improved regret analysis for UCB-N and TS-N—0
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
When Combinatorial Thompson Sampling meets Approximation Regret—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Leveraging Demonstrations to Improve Online Learning: Quality Matters—0
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits—0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
Thompson Sampling with Diffusion Generative Prior—0
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