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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 51–100 of 655 papers

TitleStatusHype
Counterfactual Inference under Thompson Sampling—0
Sparse Nonparametric Contextual Bandits—0
Bandit-Based Prompt Design Strategy Selection Improves Prompt OptimizersCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs—0
When and why randomised exploration works (in linear bandits)—0
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems—0
Contextual Thompson Sampling via Generation of Missing Data—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
Stochastically Constrained Best Arm Identification with Thompson Sampling—0
Truthful mechanisms for linear bandit games with private contexts—0
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
Generalized Bayesian deep reinforcement learning—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
Fast, Precise Thompson Sampling for Bayesian OptimizationCode0
Epinet for Content Cold Start—0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
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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