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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
Discounted Thompson Sampling for Non-Stationary Bandit Problems—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Distributed Thompson Sampling—0
Adaptive Combinatorial Allocation—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
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Bag of Policies for Distributional Deep Exploration—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
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
Dynamic Decision-Making under Model Misspecification—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization—0
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
A Copula approach for hyperparameter transfer learning—0
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