arXiv Machine Learning July 7, 2026 By Christopher B\"ulte, Yusuf Sale, Gitta Kutyniok, Eyke H\"ullermeier

Uncertainty Quantification for Regression: A Unified Framework based on kernel scores

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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.

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safety

arXiv Machine Learning

Aug 4

Nonparametric Distribution Regression Re-calibration

arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.

By \'Ad\'am Jung, Domokos M. Kelen, Andr\'as A. Bencz\'ur

rag benchmarks safety

arXiv Machine Learning

Jul 7

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

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 Machine Learning

Jul 10

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

arXiv:2603. 16481v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control.

By Amon Lahr, Anna Scampicchio, Johannes K\"ohler, Melanie N. Zeilinger

arXiv Machine Learning

Jun 19

On the QUEST for Uncertainty Quantification via Highest Density Regions

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

benchmarks safety

arXiv Machine Learning

Sep 22

Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

arXiv:1812.09632v3 Announce Type: replace Abstract: We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributi...

By Bal\'azs Csan\'ad Cs\'aji, Kriszti\'an Bal\'azs Kis

arXiv Machine Learning

Jul 7

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.

By Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu

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