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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
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
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
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
Expected Improvement-based Contextual Bandits—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—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
Thompson Sampling for Gaussian Entropic Risk Bandits—0
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Blind Exploration and Exploitation of Stochastic Experts—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—0
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection—0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Online Multi-Armed Bandits with Adaptive Inference—0
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