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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 126–150 of 655 papers

TitleStatusHype
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Belief Flows of Robust Online Learning—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Blind Exploration and Exploitation of Stochastic Experts—0
Bootstrapped Thompson Sampling and Deep Exploration—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
Calibrated Fairness in Bandits—0
A Note on Information-Directed Sampling and Thompson Sampling—0
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
Causal Bandits without prior knowledge using separating sets—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Bayesian Quantile and Expectile Optimisation—0
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