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: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: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: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.
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:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
arXiv:2505. 02257v2 Announce Type: replace-cross Abstract: In regions lacking medically certified causes of death, verbal autopsy (VA) is a widely used tool to ascertain the cause of death through interviews with caregivers.
arXiv:2509. 22352v3 Announce Type: replace Abstract: Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death.
The paper investigates efficient learning of survival analysis models when data are subject to outcome-dependent truncation and right-censoring. It demonstrates that Cox regression can be trained with first‑order optimization when the baseline hazard is known, but joint learning of the baseline hazard introduces strict suboptimal local minima. Experiments on four datasets show that accounting for truncation reduces prediction bias and yields orders‑of‑magnitude speedups over expectation maximization.
The paper explores how to adapt tabular foundation models (TabFMs) for censored time‑to‑event prediction by linking them with CoxPH and DeepHit and revising training procedures. It evaluates zero‑shot, classification‑based fine‑tuning, and survival‑head adaptations across 74 single‑risk and 4 competing‑risk datasets, finding that zero‑shot works best on small datasets while supervised adaptation excels as data grows. The study shows that the choice of adaptation interface and data regime critically influences TabFM transfer performance.
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.