Abstract Machine learning algorithms often require large training sets to perform well, but
labeling such large amounts of data is not always feasible, as in many applications …

The severity of coronary artery disease can be assessed invasively using the Fractional
Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the …

Graph-guided semi-supervised learning (SSL) has gained popularity in several network
science applications, including biological, social, and financial ones. SSL becomes …

3D reconstruction is a fundamental task in robotics that gained attention due to its major
impact in a wide variety of practical settings, including agriculture, underwater, and urban …

Assessing set-membership and evaluating distances to the related set boundary are
problems of widespread interest, and can often be computationally challenging. Seeking …

In many application domains including medical imaging, experimental design, as well as
robotics, labeled data are expensive to acquire while unlabeled samples are abundant …

Graph-guided learning has well-documented impact in a gamut of network science
applications. A prototypical graph-guided learning task deals with semi-supervised learning …

Cardiovascular disease (CVD) poses a significant global health challenge, and accurate
inference methods are vital for early detection and intervention. However, the quality of …

In this work, we consider the problem of imputing signals defined on the nodes of a product
graph from the subset of observations. To this end, we focus on learning their predictive …

Abstract Machine learning (ML) has gained popularity due to its well-documented merits in
several inference tasks across diverse applications including healthcare, robotics and …