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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 376–400 of 655 papers

TitleStatusHype
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Incentivizing Combinatorial Bandit Exploration—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
Incorporating Behavioral Constraints in Online AI Systems—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems—0
KLUCB Approach to Copeland Bandits—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Latent Bandits Revisited—0
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