The random dot product graph (RDPG) is an independent-edge random graph that is
analytically tractable and, simultaneously, either encompasses or can successfully …

Recent works in self-supervised learning have advanced the state-of-the-art by relying on
the contrastive learning paradigm, which learns representations by pushing positive pairs, or …

We consider unsupervised domain adaptation (UDA), where labeled data from a source
domain (eg, photos) and unlabeled data from a target domain (eg, sketches) are used to …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

We investigate the degree to which impact in science and technology is associated with
surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs …

Recovering low-rank structures via eigenvector perturbation analysis is a common problem
in statistical machine learning, such as in factor analysis, community detection, ranking …

Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather
than attempting to cluster points in their native domain, one constructs a (usually sparse) …

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

While many statistical models and methods are now available for network analysis,
resampling of network data remains a challenging problem. Cross-validation is a useful …

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we
study a version of constrained SC in which we try to incorporate the fairness notion …