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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 126–150 of 655 papers

TitleStatusHype
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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