This draft textbook is extracted from lecture notes from a class that I have taught
(unfortunately online, but this gave me an opportunity to write more detailed notes) during …

For distributed computing environment, we consider the empirical risk minimization problem
and propose a distributed and communication-efficient Newton-type optimization method. At …

Kernel k-means clustering can correctly identify and extract a far more varied collection of
cluster structures than the linear k-means clustering algorithm. However, kernel k-means …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

Sketching, a dimensionality reduction technique, has received much attention in the
statistics community. In this paper, we study sketching in the context of Newton's method for …

Kronecker regression is a highly-structured least squares problem $\min_ {\mathbf
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …

We study the following three fundamental problems about ridge regression:(1) what is the
structure of the estimator?(2) how to correctly use cross-validation to choose the …

Large matrices arise in many machine learning and data analysis applications, including as
representations of datasets, graphs, model weights, and first and second-order derivatives …

We consider a least squares regression problem where the data has been generated from a
linear model, and we are interested to learn the unknown regression parameters. We …

Ridge regression is a variant of regularized least squares regression that is particularly
suitable in settings where the number of predictor variables greatly exceeds the number of …