The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

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 recent years, Machine Learning (ML) models have achieved remarkable success in
various domains. However, these models also tend to demonstrate unsafe behaviors …

We study the problem of learning Lyapunov-stable neural controllers which provably satisfy
the Lyapunov asymptotic stability condition within a region-of-attraction. Compared to …

In recent years, many neural network (NN) verifiers have been developed to formally verify
certain properties of neural networks such as robustness. Although many benchmarks have …

Deep neural networks (DNNs) play a crucial role in the field of machine learning,
demonstrating state-of-the-art performance across various application domains. However …

The ability to interpret Machine Learning (ML) models is becoming increasingly essential.
However, despite significant progress in the field, there remains a lack of rigorous …

In recent years, deep reinforcement learning (DRL) approaches have generated highly
successful controllers for a myriad of complex domains. However, the opaque nature of …

Deep Neural Networks (DNNs) are ubiquitous in real-world applications, yet they remain
vulnerable to errors and adversarial attacks. This work tackles the challenge of applying …

Abstract Deep Neural Networks (DNNs) are being trained and trusted for performing fairly
complex tasks, even in safety-critical applications such as autonomous driving, medical …