Big data can easily be contaminated by outliers or contain variables with heavy-tailed
distributions, which makes many conventional methods inadequate. To address this …

We provide a new computationally efficient class of estimators for risk minimization. We
show that these estimators are robust for general statistical models, under varied robustness …

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

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …

This article provides a selective overview of the recent developments in factor models and
their applications in econometric learning. We focus on the perspective of the low-rank …

As one of the most fundamental problems in machine learning, statistics and differential
privacy, Differentially Private Stochastic Convex Optimization (DP-SCO) has been …

Estimation of the covariance matrix has attracted a lot of attention of the statistical research
community over the years, partially due to important applications such as principal …

We study the generalized trace regression with a near low-rank regression coefficient matrix,
which extends notion of sparsity for regression coefficient vectors. Specifically, given a …

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 …

Big data is transforming our world, revolutionizing operations and analytics everywhere,
from financial engineering to biomedical sciences. The complexity of big data often makes …