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 …

The local and global interpretability of various ML models has been studied extensively in
recent years. However, despite significant progress in the field, many known results remain …

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

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 …

Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance
neural network verifiers and made significant advances. However, GCP-CROWN currently …

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 …

Probabilistic verification of neural networks is concerned with formally analysing the output
distribution of a neural network under a probability distribution of the inputs. Examples of …