Graph kernels are widely used for measuring the similarity between graphs. Many existing
graph kernels, which focus on local patterns within graphs rather than their global …

Wide neural networks are biased towards learning certain functions, influencing both the
rate of convergence of gradient descent (GD) and the functions that are reachable with GD …

In practical applications, we are more likely to face semi-supervised data with a small
amount of independent class label or constraint information and many unlabeled instances …

Kernel methods are a popular technique for extending linear models to handle non-linear
spatial problems via a mapping to an implicit, high-dimensional feature space. While kernel …

Existing adaptive kernel density estimators (KDEs) and kernel regressions (KRs) often
employ a data-independent kernel, such as Gaussian kernel. They require an additional …

This paper shows that adaptive kernel density estimator (KDE) can be derived effectively
from Isolation Kernel. Existing adaptive KDEs often employ a data independent kernel such …

Kernel functions are a key element in many machine learning methods to capture the
similarity between data points. However, a considerable number of these functions do not …

Analysis of large-scale sequential data has been one of the most crucial tasks in areas such
as bioinformatics, text, and audio mining. Existing string kernels, however, either (i) rely on …

Graph-structured data analysis is an increasingly popular topic with applications in many
fields. Despite rich existing works in Graph Kernels and Graph Neural Networks, most of …

Structured data modeled as graphs arise in many application domains, such as computer
vision, bioinformatics, and sociology. In this dissertation, we focus on three important topics …