4 papers · 1 filter
Scalable Decision Focused Learning via Online Trainable Surrogates
Gaetano Signorelli, Michele Lombardi
Decision support systems often rely on solving complex optimization problems that may require to estimate uncertain parameters beforehand. Recent studies have shown how using tradi…
SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
Matteo Francobaldi, Michele Lombardi, Andrea Lodi
Artificial Intelligence systems are increasingly deployed in settings where ensuring robustness, fairness, or domain-specific properties is essential for regulation compliance and…
Constrained Machine Learning Through Hyperspherical Representation
Gaetano Signorelli, Michele Lombardi
The problem of ensuring constraints satisfaction on the output of machine learning models is critical for many applications, especially in safety-critical domains. Modern approache…
SMLE: Safe Machine Learning via Embedded Overapproximation
Matteo Francobaldi, Michele Lombardi
Despite the extent of recent advances in Machine Learning (ML) and Neural Networks, providing formal guarantees on the behavior of these systems is still an open problem, and a cru…