Cyber-vulnerabilities are being exploited in a growing number of control systems. As many
of these systems form the backbone of critical infrastructure and are becoming more …

This paper proposes to explain the black-box feature of data-driven machine learning (ML)
models used for controlling power electronic converters for the first time. As the name …

Machine Learning (ML) seems to be one of the most promising solution to automate partially
or completely some of the complex tasks currently realized by humans, such as driving …

In this work we propose a graph-based learning framework to train models with provable
robustness to adversarial perturbations. In contrast to regularization-based approaches, we …

Accuracy metric has become a gold standard for measuring various models and systems.
However, the latest development and research have shown its limitations, especially during …

Machine Learning (ML) seems to be one of the most promising solution to automate partially
or completely some of the complex tasks currently realized by humans, such as driving …

In this paper we prove the existence of a fundamental trade-off between accuracy and
robustness in perception-based control, where control decisions rely solely on data-driven …

It is well known that machine learning models are vulnerable to small but cleverly designed
adversarial perturbations that can cause misclassification. While there has been major …

This paper proposes a new framework and several results to quantify the performance of
data-driven state-feedback controllers for linear systems against targeted perturbations of …

Machine Learning (ML) seems to be one of the most promising solution to automate partially
or completely some of the complex tasks currently realized by humans, such as driving …