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 301–350 of 655 papers

TitleStatusHype
Weak Signal Asymptotics for Sequentially Randomized Experiments—0
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
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
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
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
Dynamic Decision-Making under Model Misspecification—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
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
Efficient Linear Bandits through Matrix Sketching—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
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
Estimating prediction error for complex samples—0
Estimating Quality in Multi-Objective Bandits Optimization—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
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Expected Improvement-based Contextual Bandits—0
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization—0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models—0
Exploration via linearly perturbed loss minimisation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Show:102550
← PrevPage 7 of 14Next →

No leaderboard results yet.