Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Motivated by the security risks of deep neural networks, such as various membership and
attribute inference attacks, differential privacy has emerged as a promising approach for …

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT)
and machine learning technologies have been widely used to achieve the intelligent control …

Pairwise learning refers to learning tasks where the loss function depends on a pair of
instances. It instantiates many important machine learning tasks such as bipartite ranking …

Recently, there is a growing interest in studying pairwise learning since it includes many
important machine learning tasks as specific examples, eg, metric learning, AUC …

In this paper, we study the sparse linear regression problem under the Local Differential
Privacy (LDP) model. We first show that polynomial dependency on the dimensionality $ p …

Pairwise learning has recently received increasing attention since it subsumes many
important machine learning tasks (eg AUC maximization and metric learning) into a unifying …

Metric learning aims at automatically learning a distance metric from data so that the precise
similarity between data instances can be faithfully reflected, and its importance has long …

Instead of learning with pointwise loss functions, learning with pairwise loss functions
(pairwise learning) has received much attention recently as it is more capable of modeling …

Area under the ROC curve (AUC) is a widely used performance measure for imbalanced
classification. Oftentimes, the ubiquitous imbalanced data such as financial records from …