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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 176–200 of 655 papers

TitleStatusHype
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
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
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
Discounted Thompson Sampling for Non-Stationary Bandit Problems—0
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