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 91–100 of 655 papers

TitleStatusHype
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Optimization-Driven Adaptive Experimentation—0
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based HeuristicCode0
Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systemsCode0
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback—0
Thompson Sampling Itself is Differentially Private—0
Scalable Exploration via Ensemble++Code0
Show:102550
← PrevPage 10 of 66Next →

No leaderboard results yet.