Paper Overview
Field: Machine Learning Authors: Shuai Huang, Zhe Qu, Zhaowei Hua et al. (6 authors) Posted: 2026-08-17 arXiv: 2608.16864
Abstract
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. The authors propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones. They also establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels.
Across 27 simulation settings and six cohorts, CNQ achieves lower pinball loss than quantile-, hazard-, and tree-based competitors when the conditional distribution is asymmetric. In two clinical case studies (METABRIC breast cancer; FLCHAIN population mortality), the framework recovers covariate effects that vary across the survival distribution—effects that would be hidden by a single hazard ratio—and produces coherent individualized quantile milestones.
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