Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

Technological advances have dramatically expanded our ability to probe multi-neuronal
dynamics and connectivity in the brain. However, our ability to extract a simple conceptual …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …

Random matrices now play a role in many areas of theoretical, applied, and computational
mathematics. Therefore, it is desirable to have tools for studying random matrices that are …

Students in statistics or data science usually learn early on that when the sample size n is
large relative to the number of variables p, fitting a logistic model by the method of maximum …

We prove a universality theorem for learning with random features. Our result shows that, in
terms of training and generalization errors, a random feature model with a nonlinear …

In the past five years, deep learning methods have become state-of-the-art in solving various
inverse problems. Before such approaches can find application in safety-critical fields, a …

Dual function radar communications (DFRC) systems are attractive technologies for
autonomous vehicles, which utilize electromagnetic waves to constantly sense the …

We consider a model for logistic regression where only a subset of features of size is used
for training a linear classifier over training samples. The classifier is obtained by running …