In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra …

We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

The classical development of neural networks has primarily focused on learning mappings
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

We analyze graph smoothing with mean aggregation, where each node successively
receives the average of the features of its neighbors. Indeed, it has quickly been observed …

In many clustering problems, we have access to multiple views of the data each of which
could be individually used for clustering. Exploiting information from multiple views, one can …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Semi-supervised learning is a learning paradigm concerned with the study of how
computers and natural systems such as humans learn in the presence of both labeled and …

We propose a spectral clustering algorithm for the multi-view setting where we have access
to multiple views of the data, each of which can be independently used for clustering. Our …

We study the convergence of the graph Laplacian of a random geometric graph generated
by an iid sample from am-dimensional submanifold MM in R^ d R d as the sample size n …