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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 301–350 of 655 papers

TitleStatusHype
Risk and optimal policies in bandit experiments—0
Safe Linear Leveling Bandits—0
Observation-Free Attacks on Stochastic Bandits—0
Doubly Robust Thompson Sampling with Linear Payoffs—0
Optimizing Conditional Value-At-Risk of Black-Box FunctionsCode0
Adaptive Gating for Single-Photon 3D Imaging—0
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision MedicineCode0
Hierarchical Bayesian Bandits—0
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle—0
Maillard Sampling: Boltzmann Exploration Done Optimally—0
Online Learning of Energy Consumption for Navigation of Electric Vehicles—0
Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling—0
Variational Bayesian Optimistic Sampling—0
Differentially Private Federated Bayesian Optimization with Distributed Exploration—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Batched Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Expected Improvement-based Contextual Bandits—0
Deep Exploration for Recommendation Systems—0
Vaccine allocation policy optimization and budget sharing mechanism using Thompson samplingCode0
Online Learning of Network Bottlenecks via Minimax Paths—0
Machine Learning for Online Algorithm Selection under Censored FeedbackCode0
Thompson Sampling for Bandits with Clustered Arms—0
A Unifying Theory of Thompson Sampling for Continuous Risk-Averse BanditsCode0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
Scalable regret for learning to control network-coupled subsystems with unknown dynamics—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Debiasing Samples from Online Learning Using Bootstrap—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
No Regrets for Learning the Prior in Bandits—0
Metalearning Linear Bandits by Prior Update—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
Random Effect Bandits—0
Thompson Sampling for Unimodal Bandits—0
Thompson Sampling with a Mixture Prior—0
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Parallelizing Thompson Sampling—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Asymptotically Optimal Bandits under Weighted Information—0
Diffusion Approximations for Thompson Sampling—0
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