Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining
highdimensional, low-precision, distributed representations of data. These representations …

This paper gives a review of concentration inequalities which are widely employed in non-
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …

We address the problem of Federated Learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

The phenomenon of benign overfitting is one of the key mysteries uncovered by deep
learning methodology: deep neural networks seem to predict well, even with a perfect fit to …

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

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 that the ordinary least-squares (OLS) estimator attains nearly minimax optimal
performance for the identification of linear dynamical systems from a single observed …

This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …

Self-supervised representation learning solves auxiliary prediction tasks (known as pretext
tasks), that do not require labeled data, to learn semantic representations. These pretext …

This paper investigates the theoretical foundations of the t-distributed stochastic neighbor
embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data …