arXiv Machine Learning
Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
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.
arXiv Machine Learning
Jul 21
Tabular Foundation Models Can Do Survival Analysis
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.
arXiv Machine Learning
Oct 7
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
arXiv:2610.08694v1 Announce Type: new Abstract: Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature corre...
arXiv AI
Jun 11
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
arXiv Machine Learning
Oct 16
SurvICL: a Foundation Model for in-context Survival Analysis
SurvICL is a tabular foundation model designed for in-context survival analysis, predicting survival curves directly from data. It is pretrained on synthetic datasets generated via a novel flexible proportional‑odds prior and trained using a plain quantile regression loss, enabling it to handle uncensored event times. On a new benchmark of 103 survival datasets, SurvICL outperforms 20 baseline models—including the classic Cox model—without any tuning, achieving superior integrated Brier scores on 63 held‑out datasets.
arXiv AI
Jun 3
Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models
arXiv:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.