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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 551–600 of 655 papers

TitleStatusHype
Adaptive Sensor Placement for Continuous Spaces—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
A Formal Solution to the Grain of Truth Problem—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
Aligning AI Agents via Information-Directed Sampling—0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
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
An Empirical Evaluation of Thompson Sampling—0
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders—0
An improved regret analysis for UCB-N and TS-N—0
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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