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.
arXiv:2605.28165v2 Announce Type: replace Abstract: Robust learning methods such as distributionally robust optimization, vicinal risk minimization, Mixup, and label smoothing address different sourc...
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
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.
arXiv:2610.01028v1 Announce Type: cross Abstract: Machine learning models often suffer performance degradation under subpopulation shift, particularly when spurious correlations cause models to rely...
arXiv:2605. 23268v2 Announce Type: replace-cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed.
arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between...
arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.
arXiv:2606. 25004v1 Announce Type: new Abstract: In machine learning, model certification has been identified as an important method for gaining assurance about a model's trustworthiness and quality.
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
arXiv:2608. 07953v1 Announce Type: new Abstract: Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distribution (OOD) samples and predict uncertainty levels.
The paper addresses the problem of evaluating deployed machine learning models when their input data distribution shifts, which can arise from changes in the underlying population or from data integrity issues in the feature pipeline. It proposes an online model‑testing framework that reproduces the detected shift on a controlled baseline with known labels, distinguishing between the two mechanisms of shift to estimate current performance and potential gains from repairing data integrity problems. Experiments on real‑world datasets with simulated faults demonstrate that separating the two sources of shift generally improves performance estimates, and a churn‑prediction case study shows how these tests can guide monitoring and repair prioritization.