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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 51–75 of 655 papers

TitleStatusHype
Counterfactual Inference under Thompson Sampling—0
Sparse Nonparametric Contextual Bandits—0
Bandit-Based Prompt Design Strategy Selection Improves Prompt OptimizersCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs—0
When and why randomised exploration works (in linear bandits)—0
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems—0
Contextual Thompson Sampling via Generation of Missing Data—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings—0
Stochastically Constrained Best Arm Identification with Thompson Sampling—0
Truthful mechanisms for linear bandit games with private contexts—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
Generalized Bayesian deep reinforcement learning—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
Fast, Precise Thompson Sampling for Bayesian OptimizationCode0
Epinet for Content Cold Start—0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
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