ML-VERIFICATION.COM

As data and computing infrastructures become increasingly abundant, machine learning (ML) systems are applied to ever more problems. However, for safety-critical domains, their high performance alone is not enough: applications like autonomous driving, robotics control, and medical imaging require rigorous safety guarantees. Unfortunately, many modern ML approaches lack such guarantees, precluding their adaptation. As a result, a growing body of work on formally verifying the behavior of ML systems and leveraging ML for the formal verification of classical software has emerged. While both of these communities consider the intersection of ML and formal methods, their different target audiences and, thus, conferences of choice have limited their interaction so far... Our proposed Workshop on Formal Verification and Machine Learning (WFVML) aims to bridge this gap between the formal verification and ML communities. On one hand, ML techniques offer high performance but struggle to provide..