arXiv Machine Learning By Meixu Chen, Kai Wang, Jing Wang

Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction

Read the original on arXiv Machine Learning →

arXiv:2607. 16802v1 Announce Type: new Abstract: Deep survival models are evaluated almost exclusively by the concordance index (C-index), yet they are commonly trained using likelihood objectives such as the Cox partial likelihood, discrete-time negative log-likelihood, and DeepHit likelihood.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 4

SurvPFN: Towards Foundation Models for Survival Predictions

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)