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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 51–75 of 655 papers

TitleStatusHype
Optimizing Posterior Samples for Bayesian Optimization via RootfindingCode1
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks—0
Distributed Thompson sampling under constrained communicationCode0
Aligning AI Agents via Information-Directed Sampling—0
Queueing Matching Bandits with Preference FeedbackCode0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Gaussian Process Thompson Sampling via Rootfinding—0
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling ParadoxCode0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Partially Observable Contextual Bandits with Linear Payoffs—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Sliding-Window Thompson Sampling for Non-Stationary Settings—0
Multi-Task Combinatorial Bandits for Budget Allocation—0
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—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
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