The paper investigates efficient learning of survival analysis models when data are subject to outcome‑dependent truncation and right‑censoring. It shows that Cox regression can be trained with first‑order methods when the baseline hazard is known, but joint estimation of the baseline hazard introduces non‑convexity and local minima. Experiments on four datasets demonstrate that accounting for truncation reduces bias, works with neural networks, and yields orders‑of‑magnitude speedups over expectation‑maximization.
arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.
By Samuel B\"ohm (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany), Lennart Purucker (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Frank Hutter (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Pascal Schlosser (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany)
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
By Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke
arXiv:2601. 22259v2 Announce Type: replace Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event of interest occurs.
By Da In Kim, Wei Siang Lai, Kelly W. Zhang
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form.