Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Computer vision applications like traffic monitoring, security checks, self-driving cars,
medical imaging, etc., rely heavily on machine learning models. It raises an essential …

We survey the adversarial robustness of neural networks from the perspective of Lipschitz
calculus in a unifying fashion by expressing models, attacks and safety guarantees, that is, a …

This chapter explores the foundational concept of robustness in Machine Learning (ML) and
its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The …

In contrast with ad-hoc methods for eXplainable Artificial Intelligence (XAI), formal
explainability offers important guarantees of rigor. However, formal explainability is hindered …

Ensuring fairness in machine learning (ML) is vital, especially as these models are
increasingly used in socially critical financial decision-making processes such as credit …

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

This paper systematically analyzes the security risks associated with artificial intelligence
(AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for …

In the field of robotics and automation, navigation systems based on Large Language
Models (LLMs) have recently shown impressive performance. However, the security aspects …

Adversarial training (AT) is one of the most effective ways for deep neural network models to
resist adversarial examples. However, there is still a significant gap between robust training …