arXiv:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.
arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.
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: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:2510. 04421v3 Announce Type: replace-cross Abstract: Survival analysis provides statistical methods to model the time until an event occurs.
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
arXiv:2604. 08870v3 Announce Type: replace-cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration.
arXiv:2606. 11267v1 Announce Type: new Abstract: Data leakage -- contamination of a model with information unavailable at baseline -- is the dominant reproducibility failure in machine-learning-based science, yet detection tools require training code, external data, or domain expertise.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
arXiv:2607. 19526v1 Announce Type: new Abstract: "Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.