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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 51–75 of 655 papers

TitleStatusHype
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
An Analysis of Ensemble Sampling—0
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
An Empirical Evaluation of Thompson Sampling—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
A Bayesian Choice Model for Eliminating Feedback Loops—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
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