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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 201–225 of 655 papers

TitleStatusHype
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Leveraging Demonstrations to Improve Online Learning: Quality Matters—0
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits—0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
Thompson Sampling with Diffusion Generative Prior—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Neural Bandits for Data Mining: Searching for Dangerous PolypharmacyCode0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning ApproachCode0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
Sample-Then-Optimize Batch Neural Thompson SamplingCode1
Deep Active Ensemble Sampling For Image Classification—0
The Typical Behavior of Bandit Algorithms—0
Cost Aware Asynchronous Multi-Agent Active Search—0
Thompson Sampling with Virtual Helping Agents—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
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