发表机构
Pohang University of Science and Technology（浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异质群体中分布偏移和异常值污染导致的生存模型性能下降问题，提出结合外部最小化与内部最大化的分布鲁棒框架，在模拟和基准实验中稳定训练并显著提升最差组性能。
AI 中文摘要
在分布偏移下学习鲁棒的生存模型是许多应用中重要但具有挑战性的问题。在异质群体中，一个在平均意义上表现良好的模型可能在特定子群体上表现不佳，而当训练数据被异常值污染时，这一问题变得更加严重。本文提出了一种新颖的分布鲁棒生存分析框架，同时处理潜在的子群体偏移和异常值污染。所提出的方法结合了外部最小化——通过减少污染样本的影响来选择精细的名义分布——以及内部最大化——聚焦于最具挑战性的子群体。该公式直接适应不可分解的生存损失，同时保留样本间的交互，包括Cox负偏对数似然的风险集结构。我们开发了一种基于梯度的交替算法，其外部更新由内部最大化的KKT条件推导而来。在模拟数据和两个生存基准上的实验表明，当子群体偏移和异常值污染同时发生时，所提出的方法保持鲁棒性。它在污染设置中稳定了训练，并在线性和非线性生存模型中显著改善了最差组性能，同时保持具有竞争力且有时更优的整体性能。
英文摘要
Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a model that performs well on average may still perform poorly on certain subpopulations, and this issue becomes even more severe when the training data are contaminated by outliers. In this paper, we propose a novel distributionally robust framework for survival analysis that jointly addresses latent subpopulation shift and outlier contamination. The proposed method combines an outer minimization that selects a refined nominal distribution by reducing the influence of contaminated samples and an inner maximization that focuses on the most challenging subpopulation. This formulation directly accommodates non-decomposable survival losses while preserving interactions across samples, including the risk-set structure of the Cox negative partial log-likelihood. We develop an alternating gradient-based algorithm with outer updates derived from the KKT conditions of the inner maximization. Experiments on simulated data and two survival benchmarks demonstrate that the proposed method remains robust when subpopulation shift and outlier contamination occur simultaneously. It stabilizes training in contaminated settings and substantially improves worst-group performance across both linear and nonlinear survival models, while maintaining competitive and sometimes superior overall performance.
Comments36 pages, including appendices
