SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 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
Show:102550
← PrevPage 23 of 27Next →

No leaderboard results yet.