arXiv:2602. 08470v3 Announce Type: replace Abstract: Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions.
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 Machine Learning.
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
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...
arXiv:2609.14065v1 Announce Type: new Abstract: When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop be...
arXiv:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.
The paper introduces egRUE, an explainable uncertainty estimation method that merges uncertainty quantification with feature‑level explanations for medical AI predictions. egRUE incorporates prediction explanations into its uncertainty calculation and decomposes uncertainty into contributions from individual features. Experiments and a user study with medical experts show that egRUE improves reliability, interpretability, and calibrated trust compared to existing methods.