arXiv:2605. 30119v2 Announce Type: replace-cross Abstract: Survival analysis concerns the task of predicting the time until an event occurs.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2609.23780v1 Announce Type: new Abstract: Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule...
arXiv:2606. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data.
arXiv:2607. 01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization.
DynSHAP is a SHAP-based framework designed for dynamic survival analysis, extending marginal SHAP estimators to handle time–feature pairs as players in the Shapley game. It introduces Temporal DynSHAP, which models linear dependencies across time and uses conditional sampling to improve explanations. Experiments on synthetic data and two real-world clinical datasets show that Temporal DynSHAP more accurately recovers temporally dependent features and provides faithful attributions for two DSA architectures, enabling medical experts to identify which patient information influenced predictions and when.
arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.