Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

Deep learning's performance has been extensively recognized recently. Graph neural
networks (GNNs) are designed to deal with graph-structural data that classical deep …

In recent years, the machine learning community has witnessed a tremendous growth in the
development of kernel-based learning algorithms. However, the performance of this class of …

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that
incorporates gene pathway information into feature selection. The proposed formulation is …

Graph-based approaches have been most successful in semisupervised learning. In this
paper, we focus on label propagation in graph-based semisupervised learning. One …

We introduce new Perceptron-based algorithms for the online multitask binary classification
problem. Under suitable regularity conditions, our algorithms are shown to improve on their …

Abstract How might one``reduce''a graph? That is, generate a smaller graph that preserves
the global structure at the expense of discarding local details? There has been extensive …

A foundational problem in semi-supervised learning is the construction of a graph
underlying the data. We propose to use a method which optimally combines a number of …

We study the problem of learning a binary classifier on the vertices of a graph. In particular,
we consider classifiers given by\emph {monophonic halfspaces}, partitions of the vertices …

Graph neural networks (GNNs) have emerged as the standard method for numerous tasks
on graph-structured data such as node classification. However, real-world graphs are often …