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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 576–600 of 655 papers

TitleStatusHype
An Information-Theoretic Analysis for Thompson Sampling with Many Actions—0
An Information-Theoretic Analysis of Thompson Sampling—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles—0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
A Note on Information-Directed Sampling and Thompson Sampling—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Approximate information for efficient exploration-exploitation strategies—0
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
A study of Thompson Sampling with Parameter h—0
Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits—0
Asymptotically Optimal Bandits under Weighted Information—0
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
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