arXiv：cs.LG· Itai Zilberstein, Ioannis Anagnostides, Arman Kilic, Tuomas Sandholm·· 11 小时前
结果依赖截断与删失下生存分析模型的高效学习
Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring
AI 导读
针对训练数据因标签而出现的结果依赖截断问题，研究者证明了在基线风险已知时，Cox 回归可借助一阶优化高效学习，尽管目标函数非凸；而当基线风险需联合学习时，可构造出负对数似然含严格次优局部极小值的可识别模型。在四个生存数据集上，考虑截断降低了朴素 Cox 回归带来的预测偏差，并可扩展至神经网络，相比标准期望最大化方法取得数量级加速。
正文
Abstract:Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but practical datasets violate this assumption. We focus on outcome-dependent truncation in which an observation appears in the training data depending on its label. Our work connects two largely distinct literatures: efficient learning under truncation and survival analysis. Existing methods for truncated survival analysis do not characterize the optimization landscape and instead rely on iterative procedures such as expectation maximization. We address this gap and prove that Cox regression can be efficiently learned under truncated and right-censored data using first-order optimization when the baseline hazard is known (despite nonconvexity). In contrast, when the baseline hazard is learned jointly, we construct identifiable and correctly specified models whose negative log-likelihood contains strict suboptimal local minima. On experiments with four survival datasets, we show that accounting for truncation reduces the prediction bias induced by naive Cox regression and extends to neural networks. We also show orders of magnitude speedups compared to standard expectation maximization.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12538 [cs.LG] |
| (or arXiv:2610.12538v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12538 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Itai Zilberstein [view email]
[v1]
Thu, 8 Oct 2026 13:32:56 UTC (301 KB)
来源：arXiv：cs.LG · arxiv.org