Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Recent research has proposed various methods to formally verify neural networks against
minimal input perturbations; this verification task is also known as local robustness …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in
machine learning due to its successful applications to real-world and complex systems …

Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the" black box" nature of DRL agents …

In this short paper, we present our ongoing work on the veriFIRE project—a collaboration
between industry and academia, aimed at using verification for increasing the reliability of a …

Differentiable logics (DL) have recently been proposed as a method of training neural
networks to satisfy logical specifications. A DL consists of a syntax in which specifications …

The behavior of neural networks (NNs) on previously unseen types of data (out-of-
distribution or OOD) is typically unpredictable. This can be dangerous if the network's output …

Extensive research on formal verification of machine learning systems indicates that
learning from data alone often fails to capture underlying background knowledge such as …