Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Recent regulations concerning the Right to be Forgotten have greatly influenced the
operation of recommender systems, because users now have the right to withdraw their …

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to
various attacks. In this article, we concentrate on membership inference attacks and propose …

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 …

Machine Learning (ML) techniques have been applied to many real-world applications to
perform a wide range of tasks. In practice, ML models are typically deployed as the black …

Deep Learning (DL) techniques have gained significant importance in the recent past due to
their vast applications. However, DL is still prone to several attacks, such as the Membership …

Recommender systems are important for providing personalized services to users, but the
vast amount of collected user data has raised concerns about privacy (eg, sensitive data) …

Transfer learning is widely used for transferring knowledge from a source domain to the
target domain where the labeled data is scarce. Recently, deep transfer learning has …

In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …