arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.
By Ange-Cl\'ement Akazan, Ineza Remy Mugenga, Abebe Geletu, Jean Medard Ngnotchouye, Issa Karambal
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt
The paper introduces SHAP concentration as a pre‑deployment diagnostic for detecting when conformal prediction will fail under distribution shift, specifically in gradient‑boosted classifiers. Using a COVID‑19 supply‑chain case study, the authors show that higher feature‑importance concentration correlates with larger drops in coverage, while standard shift detectors cannot differentiate between catastrophic and robust outcomes. The diagnostic is validated on additional datasets, and a formal theorem links concentration to worsening conformity‑score bounds, though it does not capture global‑sensitivity failures in neural networks.
By Chorok Lee
arXiv:2609.13914v1 Announce Type: new
Abstract: Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn...
By Masoumeh Zareapoor
arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).
By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis
arXiv:2510. 25573v2 Announce Type: replace-cross Abstract: Machine learning approaches for image classification have led to impressive advances in that field.
By Christopher T. Franck, Anne R. Driscoll, Zoe Szajnfarber, William H. Woodall