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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 151–200 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
Thompson Exploration with Best Challenger Rule in Best Arm Identification—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—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
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Combinatorial Neural Bandits—0
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
Discounted Thompson Sampling for Non-Stationary Bandit Problems—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
An improved regret analysis for UCB-N and TS-N—0
Trajectory-oriented optimization of stochastic epidemiological modelsCode0
Neural Exploitation and Exploration of Contextual BanditsCode1
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms—0
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
When Combinatorial Thompson Sampling meets Approximation Regret—0
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