The paper introduces Pairwise Difference Learning (PDL) for classification, extending a meta‑learning technique originally designed for regression. PDL learns a function that predicts the difference between outcomes of two instances; predictions for a new instance are obtained by averaging differences with all training examples. Experiments show that the resulting PDL classifier surpasses state‑of‑the‑art methods, and the authors release a user‑friendly Python implementation.
By Mohamed Karim Belaid, Maximilian Rabus, Eyke H\"ullermeier
arXiv:2603. 27270v2 Announce Type: replace Abstract: Credal sets, i.
By Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann, Michele Caprio, Krikamol Muandet, Humberto Bustince, S\'ebastien Destercke, Eyke H\"ullermeier, Yusuf Sale
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.
By Christopher B\"ulte, Yusuf Sale, Timo L\"ohr, Paul Hofman, Gitta Kutyniok, Eyke H\"ullermeier
arXiv:2606. 10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings.
By Arthur Hoarau
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
By Jakub Paplh\'am, Willem Waegeman, Eyke H\"ullermeier, Vojt\v{e}ch Franc
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.
By Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett, Farnoush Banaei-Kashani
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
By H. Martin Gillis, Isaac Xu, Thomas Trappenberg
The paper introduces SD-Pcomp learning, a binary classification framework that jointly utilizes Similarity/Dissimilarity (SD) labels and Pairwise Comparison (Pcomp) labels from instance pairs. It proposes an objective function that can be decomposed into either an SD estimator plus ordering information or a Pcomp estimator plus pair-type information, thereby integrating complementary relational cues. Experiments on eight datasets demonstrate that combining both label types improves classification accuracy and AUC compared to using either alone or a simple convex combination.
By Tomoya Tate, Kosuke Sugiyama, Masato Uchida
arXiv:2607. 15196v1 Announce Type: cross Abstract: We present a novel viewpoint for uncertainty quantification.
By Raghad Alamri, Michele Caprio, Gavin Brown
The paper introduces a method for risk‑averse machine learning that uses credal sets—collections of probability distributions—to capture epistemic uncertainty about the true loss distribution. By producing predictions as credal sets and applying a novel decision rule that selects a single predictive distribution for conditional value‑at‑risk (CVaR) minimization, the approach aims to reliably avoid catastrophic decisions across classification, distribution shift, and reinforcement learning scenarios while maintaining near‑optimal expected performance.
By Timo L\"ohr, Paul Hofman, Maximilian Muschalik, Eyke H\"ullermeier
The paper introduces CF‑SNN, a conformalized simplicial neural network framework that provides uncertainty quantification for predictions on simplicial complexes. It generates simultaneous prediction sets for multiple targets, guaranteeing that each set contains the true label with a user‑specified coverage probability. Experiments across diverse SC prediction tasks show that CF‑SNN achieves the desired coverage while keeping prediction sets small and informative.
By Lekun Wang, Jaidev Goel, Yulia R. Gel, Yao Xie
arXiv:2606. 11988v1 Announce Type: new Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling.
By Yusuf Sale, Christopher B\"ulte, Felix Czaja, Joshua Stiller, Eyke H\"ullermeier