Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed
to design novel types of artificial neurons and introduce neuronal diversity into artificial …

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness,
and robustness. Unfortunately, on the one hand, sound and complete certification algorithms …

Privacy-preserving machine learning has achieved breakthrough advances in collaborative
training of machine learning models, under strong information-theoretic privacy guarantees …

In recent years, many formal methods and tools for verifying Machine Learning (ML)
algorithms have been developed. However, they are still in research stage and therefore it is …

We study auditing total data withdrawal, the case in which a user requests the exclusion of
their data from both the training and test data for some machine learning task. This approach …

In the robustness verification of neural networks, formal methods have been used to give
deterministic guarantees for neural networks. However, recent studies have found that the …