arXiv:2510. 04421v3 Announce Type: replace-cross Abstract: Survival analysis provides statistical methods to model the time until an event occurs.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
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.
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: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:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.