Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

Self-supervised learning is an emerging machine learning (ML) paradigm. Compared to
supervised learning which leverages high-quality labeled datasets, self-supervised learning …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and
draw inferences from large scale graph-structured data in various application settings such …

The widespread use of deep learning technology across various industries has made deep
neural network models highly valuable and, as a result, attractive targets for potential …

Abstract Model extraction attacks are considered to be a significant avenue of vulnerability in
machine learning. In model extraction attacks, the attacker repeatedly queries a victim model …

Graph Neural Networks (GNNs) have advanced the field of machine learning by utilizing
graph-structured data, which is ubiquitous in the real world. GNNs have applications in …

Graph neural networks (GNNs) have emerged as the state of the art for a variety of graph-
related tasks and have been widely commercialized in real-world scenarios. Behind its …