arXiv:2506. 02075v3 Announce Type: replace-cross Abstract: The current state of evaluation in survival analysis is plagued by the persistent use of evaluation metrics in ways that are misaligned with the stated modeling objective.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
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. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.
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:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.
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.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.