Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

An optimal statistical and computational framework for generalized tensor estimation Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 1–29 https://doi.org/10.1214/21-AOS2061 © Institute of …

In this paper, we develop a novel procedure for low-rank tensor regression, namely
Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind …

Differential graphical models are designed to represent the difference between the
conditional dependence structures of two groups, and thus are of particular interest for …

Sparse factorization of a large matrix is fundamental in modern statistical learning. In
particular, the sparse singular value decomposition has been utilized in many multivariate …

Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-
seq data for improved downstream analyses such as developmental trajectory analyses, but …

We propose a new algorithm for the problem of recovering data that adheres to multiple,
heterogenous low-dimensional structures from linear observations. Focussing on data …

The study of functional networks of the human brain has been of significant interest in
cognitive neuroscience for over two decades, albeit they are typically extracted at a single …

This work studies estimation of sparse principal components in high dimensions.
Specifically, we consider a class of estimators based on kernel PCA, generalizing the …

Traditional works on community detection from observations of information cascade assume
that a single adjacency matrix parametrizes all the observed cascades. However, in reality …