结果加权学习的一般理论：用于个体化治疗规则
摘要
个人化医疗旨在针对个体患者制定治疗方案，尤其是在人们对疗法反应存在差异时。关键目标是学习个体化治疗规则，以从患者特征中推荐最佳治疗方案。结果加权学习(OWL)是重要框架，因为它将任务重新表述为针对临床获益的加权分类问题，并使用现代机器学习工具。现有OWL理论主要关注特定的代理损失和高斯核。允许可调平滑度并更好匹配实际数据结构的Matern核常更适用，并将高斯核作为特殊情况。本 work develops a general relationship between population 0-1 risk and risks from a broad class of nonnegative surrogate losses using a constrained variational transformation. The transform simplifies for convex losses and provides simple expressions for certain nonconvex losses. A condition is established that ensures a nontrivial upper bound on the excess 0-1 risk. The paper establishes convergence rates for kernel based OWL under smoothness conditions with Matern kernels or geometric noise conditions with Gaussian kernels for both convex and nonconvex losses. It also proposes two iteratively reweighted convex optimization algorithms. Simulations and an application to ACTG 175 show strong performance.
引用
@article{arxiv.2603.10204,
title = {A General Theory of Outcome Weighted Learning for Individualized Treatment Rules},
author = {Zhu Wang},
journal= {arXiv preprint arXiv:2603.10204},
year = {2026}
}