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. 26990v1 Announce Type: cross Abstract: Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on such metrics does not necessarily imply high utility in downstream decisions.
By Annika Schneider, Tommy Rochussen, Joshua Stiller, Vincent Fortuin
arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.
By Sam Goring, Tom Kuipers, Nicola Paoletti, David S. Watson
arXiv:2510. 25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification-focused.
By Christopher B\"ulte, Yusuf Sale, Gitta Kutyniok, Eyke H\"ullermeier
arXiv:2606. 01427v1 Announce Type: cross Abstract: Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning.
By Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh, Oriol Vendrell-Gallart, Ramin Bostanabad
arXiv:2606. 03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode.
By Areeb Gani, Asal Meskin, Gabrielle Kaili-May Liu, Arman Cohan