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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 151–175 of 655 papers

TitleStatusHype
Little Exploration is All You Need—0
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingCode1
Making RL with Preference-based Feedback Efficient via Randomization—0
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization—0
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental HealthCode0
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised PretrainingCode1
Optimal Exploration is no harder than Thompson Sampling—0
Module-wise Adaptive Distillation for Multimodality Foundation Models—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Thompson Exploration with Best Challenger Rule in Best Arm Identification—0
Monte-Carlo tree search with uncertainty propagation via optimal transport—0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors—0
Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted AveragesCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Bag of Policies for Distributional Deep Exploration—0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
Approximate information for efficient exploration-exploitation strategies—0
Thompson Sampling under Bernoulli Rewards with Local Differential Privacy—0
Thompson sampling for improved exploration in GFlowNets—0
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits—0
Scalable Neural Contextual Bandit for Recommender Systems—0
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