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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 301–325 of 655 papers

TitleStatusHype
Approximate information for efficient exploration-exploitation strategies—0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—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
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—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
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
KLUCB Approach to Copeland Bandits—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
Causal Bandits without prior knowledge using separating sets—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
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