We review state-of-the-art formal methods applied to the emerging field of the verification of
machine learning systems. Formal methods can provide rigorous correctness guarantees on …

We present a novel method for scalable and precise certification of deep neural networks.
The key technical insight behind our approach is a new abstract domain which combines …

We present AI 2, the first sound and scalable analyzer for deep neural networks. Based on
overapproximation, AI 2 can automatically prove safety properties (eg, robustness) of …

We present a new method and system, called DeepZ, for certifying neural network
robustness based on abstract interpretation. Compared to state-of-the-art automated …

We introduce a scalable method for training robust neural networks based on abstract
interpretation. We present several abstract transformers which balance efficiency with …

We present COLT, a new method to train neural networks based on a novel combination of
adversarial training and provable defenses. The key idea is to model neural network training …

We present a novel approach for the certification of neural networks against adversarial
perturbations which combines scalable overapproximation methods with precise (mixed …

Neural networks as a machine learning technique are increasingly deployed in various
domains. Despite their performance and their continuous improvement, the deployment of …

In recent years, the notion of local robustness (or robustness for short) has emerged as a
desirable property of deep neural networks. Intuitively, robustness means that small …

Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable
to small perturbations on the inputs, which has led to safety concerns on applying DNNs to …