SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

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
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Debiasing Samples from Online Learning Using Bootstrap—0
Asymptotic Convergence of Thompson Sampling—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
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Cover Tree Bayesian Reinforcement Learning—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
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
Dynamic Decision-Making under Model Misspecification—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
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
Counterfactual Inference under Thompson Sampling—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
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
Efficient Learning in Large-Scale Combinatorial Semi-Bandits—0
Counterfactual Data-Fusion for Online Reinforcement Learners—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
Asymptotically Optimal Bandits under Weighted Information—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
A General Theory of the Stochastic Linear Bandit and Its Applications—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
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Estimating prediction error for complex samples—0
Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits—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
Convolutional Monte Carlo Rollouts in Go—0
Show:102550
← PrevPage 5 of 14Next →

No leaderboard results yet.