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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 76–100 of 655 papers

TitleStatusHype
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—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
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling ParadoxCode0
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
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
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