Historically, scalability has been a major challenge for the successful application of
semidefinite programming in fields such as machine learning, control, and robotics. In this …

Bound propagation methods, when combined with branch and bound, are among the most
effective methods to formally verify properties of deep neural networks such as correctness …

Training neural networks with verifiable robustness guarantees is challenging. Several
existing approaches utilize linear relaxation based neural network output bounds under …

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 …

Verification of neural networks enables us to gauge their robustness against adversarial
attacks. Verification algorithms fall into two categories: exact verifiers that run in exponential …

Robustness verification that aims to formally certify the prediction behavior of neural
networks has become an important tool for understanding model behavior and obtaining …

The use of neural networks in safety-critical computer vision systems calls for their
robustness certification against natural geometric transformations (eg, rotation, scaling) …

Abstract This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and
associated algorithms in future Capital Markets. New AI algorithms are constantly emerging …

We consider the problem of whether a given decision model, working with structured data,
has individual fairness. Following the work of Dwork, a model is individually biased (or …

Business reliance on algorithms is becoming ubiquitous, and companies are increasingly
concerned about their algorithms causing major financial or reputational damage. High …