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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 101–150 of 655 papers

TitleStatusHype
A Unifying Theory of Thompson Sampling for Continuous Risk-Averse BanditsCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Dynamic Assortment Selection and Pricing with Censored Preference FeedbackCode0
State-Aware Variational Thompson Sampling for Deep Q-NetworksCode0
Sub-sampling for Efficient Non-Parametric Bandit ExplorationCode0
Thompson Sampling Algorithms for Mean-Variance BanditsCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling ParadoxCode0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Bandit Learning with Implicit FeedbackCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Thompson Sampling via Local UncertaintyCode0
Trajectory-oriented optimization of stochastic epidemiological modelsCode0
Distributed Thompson sampling under constrained communicationCode0
Scalable Exploration via Ensemble++Code0
Adaptive Interventions with User-Defined Goals for Health Behavior ChangeCode0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Bayesian bandits: balancing the exploration-exploitation tradeoff via double samplingCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Double Thompson Sampling for Dueling BanditsCode0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Incentivizing Exploration In Reinforcement Learning With Deep Predictive ModelsCode0
Neural Bandits for Data Mining: Searching for Dangerous PolypharmacyCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy CriticsCode0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
An Empirical Evaluation of Thompson Sampling—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
Adaptive Gating for Single-Photon 3D Imaging—0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Batched Thompson Sampling—0
An Analysis of Ensemble Sampling—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
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