Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
E-Globe: Scalable -Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branching
Wenting Li, Saif R. Kazi, Russell Bent +2
Neural networks achieve strong empirical performance, but robustness concerns still hinder deployment in safety-critical applications. Formal verification provides robustness guara…
cs.LG2025
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
Miao Li, Michael Klamkin, Pascal Van Hentenryck +2
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising,…
cs.LG2024
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
Mohamad Fares El Hajj Chehade, Wenting Li, Brian W. Bell +3
The robustness of neural networks is crucial in safety-critical applications, where identifying a reliable input space is essential for effective model selection, robustness evalua…