most citedDirect Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026★ 1 cited

Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model

Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin +2

In engineering, uncertainty propagation aims to characterise system outputs under uncertain inputs. For interval uncertainty, the goal is to determine output bounds given interval-…

cs.LG2026

Learning Credal Ensembles via Distributionally Robust Optimization

Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin +3

Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epist…