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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 201–250 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
Bayesian Quantile and Expectile Optimisation—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization—0
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Efficient Exploration for LLMs—0
Efficient exploration of zero-sum stochastic games—0
Bandits Under The Influence (Extended Version)—0
Efficient exploration with Double Uncertain Value Networks—0
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling—0
Efficient kernelized bandit algorithms via exploration distributions—0
Efficient Learning in Large-Scale Combinatorial Semi-Bandits—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
Efficient-UCBV: An Almost Optimal Algorithm using Variance Estimates—0
Eluder Dimension and the Sample Complexity of Optimistic Exploration—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Ensemble Sampling—0
Epinet for Content Cold Start—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Estimating prediction error for complex samples—0
A Copula approach for hyperparameter transfer learning—0
Etat de l'art sur l'application des bandits multi-bras—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
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