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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 201–225 of 655 papers

TitleStatusHype
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
Double-Linear Thompson Sampling for Context-Attentive Bandits—0
Counterfactual Inference under Thompson Sampling—0
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
Double Thompson Sampling in Finite stochastic Games—0
Online Multi-Armed Bandits with Adaptive Inference—0
Doubly robust Thompson sampling for linear payoffs—0
Doubly Robust Thompson Sampling with Linear Payoffs—0
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Dual-Directed Algorithm Design for Efficient Pure Exploration—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
Dynamic Decision-Making under Model Misspecification—0
Asymptotically Optimal Bandits under Weighted Information—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization—0
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
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