Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for
deployment in a wide range of applications, such as autonomous systems, medical …

Quantum algorithms for solving a wide range of practical problems have been proposed in
the last ten years, such as data search and analysis, product recommendation, and credit …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and
thus there is a line of work aiming to provide robustness certification for NNs, such as …

Deep neural network (DNN) testing approaches have grown fast in recent years to test the
correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …

Fast and precise Lipschitz constant estimation of neural networks is an important task for
deep learning. Researchers have recently found an intrinsic trade-off between the accuracy …

We present a novel, general construction to abstractly interpret higher-order automatic
differentiation (AD). Our construction allows one to instantiate an abstract interpreter for …

Datasets can be biased due to societal inequities, human biases, under-representation of
minorities, etc. Our goal is to certify that models produced by a learning algorithm are …