Network embedding is to learn low-dimensional vector representations for nodes in a
network. It has shown to be effective in a variety of tasks such as node classification and link …

Machine learning and data mining algorithms are becoming increasingly important in
analyzing large volume, multi-relational and multi--modal datasets, which are often …

Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

We consider the vector embedding problem. We are given a finite set of items, with the goal
of assigning a representative vector to each one, possibly under some constraints (such as …

We propose a new framework, called Poisson learning, for graph based semi-supervised
learning at very low label rates. Poisson learning is motivated by the need to address the …

We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM.
The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) …

We investigate a family of regression problems in a semisupervised setting. The task is to
assign real-valued labels to a set of n sample points provided a small training subset of N …

We study the game theoretic p-Laplacian for semi-supervised learning on graphs, and show
that it is well-posed in the limit of finite labeled data and infinite unlabeled data. In particular …

We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …

We study the consistency of Lipschitz learning on graphs in the limit of infinite unlabeled
data and finite labeled data. Previous work has conjectured that Lipschitz learning is well …