arXiv:2509. 08846v2 Announce Type: replace-cross Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications.
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
arXiv:2505. 02743v3 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process.
arXiv:2504. 18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations.
arXiv:2603. 27270v2 Announce Type: replace Abstract: Credal sets, i.
arXiv:2605. 00600v2 Announce Type: replace-cross Abstract: Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling.
arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.
arXiv:2607. 15196v1 Announce Type: cross Abstract: We present a novel viewpoint for uncertainty quantification.
arXiv:2608. 07630v1 Announce Type: new Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data.
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
arXiv:2505. 19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees.
arXiv:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.