In the past decade, semi-supervised learning (SSL) over graphs has gained popularity due
to its importance in a gamut of network science applications. While most of existing SSL …

Labeled data can be expensive to acquire in several application domains, including medical
imaging, robotics, and computer vision. To efficiently train machine learning models under …

Abstract Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric
models that can be used to infer nonlinear functions with time-varying noise. In robotics, they …

Bayesian optimization (BO) has well-documented merits for optimizing black-box functions
with an expensive evaluation cost. Such functions emerge in applications as diverse as …

Graph-based inference arises in a gamut of network science-related applications, including
smart transportation, climate forecasting, and neuroscience. Given observations over a …

Higher-order link prediction (HOLP) seeks missing links capturing dependencies among
three or more network nodes. Predicting high-order links (HOLs) can for instance reveal …

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

In this thesis, we specifically focus on sparse Gaussian processes (GPs) based on random
features (RFs), which offer computational efficiency as they only require matrix multiplication …

Determining the number of latent variables, or the dimensions of latent states, is a ubiquitous
problem in dimension reduction. In this paper, we introduce a novel sequential method that …