Cesare Donati

Directional Conformal Uncertainty Quantification from Learned Model Discrepancy

A conformal prediction framework for quantifying the error of physics-based predictors used in control, where simple models are preferred for synthesis, certification, and real-time use, and is extended to the multivariate case via a Minkowski-gauge score yielding a jointly calibrated disturbance set.