Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

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 …

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning
technique for dimension reduction. UMAP is constructed from a theoretical framework based …

Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML
systems that are already used in production. The main appeal of post-processing is that it …

This handbook provides a comprehensive and unified account of the main research
developments in cluster analysis. Written by active, distinguished researchers in this area …

In social settings, individuals interact through webs of relationships. Each individual is a
node in a complex network (or graph) of interdependencies and generates data, lots of data …

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 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 …

Inverse imaging problems are inherently underdetermined, and hence, it is important to
employ appropriate image priors for regularization. One recent popular prior-the graph …

Attention based models such as Transformers involve pairwise interactions between data
points, modeled with a learnable attention matrix. Importantly, this attention matrix is …