Author

Michele Caprio

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#machine learning Preprint Oct 2026

Deterministic Bounds on Accuracy and Stability of Metric-Space Regression and Classification

A geometric framework that quantifies ambiguity of the input-target relation through the average kernel size, computed with data-dependent, i.e. model-independent, feasible sets derived from worst-case input and target noise is introduced.

N. Gottschling, Jakob Gawlikowski, Michele Caprio · 0 citations

#machine learning Jul 2026

Subjective Risk Decomposition: A New View for Uncertainty Quantification

It is shown how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss, and considered a first step towards a full learning-theoretic framework for uncertainty quantification.

R. Alamri, Michele Caprio, Gavin Brown · arXiv.org · 1 citation · ⚡1