Designing neural networks with bounded Lipschitz constant is a promising way to obtain
certifiably robust classifiers against adversarial examples. However, the relevant progress …

To improve the robustness of deep classifiers against adversarial perturbations, many
approaches have been proposed, such as designing new architectures with better …

Traditionally, data selection has been studied in settings where all samples from prospective
sources are fully revealed to a machine learning developer. However, in practical data …

Cloud-based machine learning inference is an emerging paradigm where users query by
sending their data through a service provider who runs an ML model on that data and …

We consider the verification of input-relational properties defined over deep neural networks
(DNNs) such as robustness against universal adversarial perturbations, monotonicity, etc …

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI),
where agents interact with environments to learn policies for solving complex tasks. In recent …

Relational learning has gained significant attention, led by the expressiveness of Graph
Neural Networks (GNNs) on graph data. While the inherent biases in common graph data …

While prior research has proposed a plethora of methods that build neural classifiers robust
against adversarial robustness, practitioners are still reluctant to adopt them due to their …

Since the control of the Lipschitz constant has a great impact on the training stability,
generalization, and robustness of neural networks, the estimation of this value is nowadays …

Control barrier functions (CBFs) are important in safety-critical systems and robot control
applications. Neural networks have been used to parameterize and synthesize CBFs with …