Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning

DOI10.1080/01621459.2022.2138760arXiv2110.10719MaRDI QIDQ6154019

Annie Qu, Wenzhuo Zhou, Ruoqing Zhu

Publication date: 19 March 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.10719

zbMATH Keywords

reinforcement learningprecision medicinepolicy optimizationsparse policy

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• A Bernstein type inequality and moderate deviations for weakly dependent sequences
• Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
• Positive definite functions and generalizations, an historical survey
• Time bounds for selection
• High-dimensional \(A\)-learning for optimal dynamic treatment regimes
• \({\mathcal Q}\)-learning
• Introduction to empirical processes and semiparametric inference
• Exponential inequalities for the distributions of canonical U- and V-statistics of dependent observations
• On Generalized Bellman Equations and Temporal-Difference Learning
• Support Vector Machines
• The Sequential Quadratic Programming Method
• Constructing dynamic treatment regimes over indefinite time horizons
• Optimal Dynamic Treatment Regimes
• Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning
• Breaking the Curse of Dimensionality with Convex Neural Networks
• New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
• Convexity, Classification, and Risk Bounds
• Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile Health