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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
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
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—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
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Combinatorial Neural Bandits—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
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
KLUCB Approach to Copeland Bandits—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio—0
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search—0
An improved regret analysis for UCB-N and TS-N—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
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