Verification and Repair of Machine Learning Models

In these last few years, machine learning (ML) has gained incredible traction in the Artificial Intelligence community, and ML models have found successful applications in many different domains across computer science. However, it is hard to provide any formal guarantee on the behavior of ML models, and therefore their reliability is still in doubt, especially concerning their deployment in safety and security-critical applications. Verification and repair emerged as promising solutions to address some of these problems. In this dissertation, we present our contributions to these two lines of research: in particular, we focus on verifying and repairing machine-learned controllers, leveraging learning techniques to enhance the verification and repair of neural networks, and developing novel tools and algorithms for verifying neural networks. Part of our research is made available in the library pyNeVer, which provides capabilities for training, verification, and management of neural networks.