arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
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:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
The paper introduces a method for learning from multiple experts who provide interval labels, addressing both within‑label imprecision and between‑expert variation. It harmonizes diverse label vocabularies into a shared probabilistic space, retains individual intervals using a mixture of Beta distributions, and decomposes predictive uncertainty into components that are matched to their corresponding sources of label uncertainty. On sea‑ice concentration data, the approach achieves a 31% reduction in mean absolute error compared to hard‑label baselines and outperforms several aggregation and interval‑regression methods.
arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.
arXiv:2509. 21514v4 Announce Type: replace Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy.