Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …

Principal component analysis (PCA) is a fundamental primitive of many data analysis, array
processing, and machine learning methods. In applications where extremely large arrays of …

One of the most important research topics in smart grid technology is load forecasting,
because accuracy of load forecasting highly influences reliability of the smart grid systems …

Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

Transfer learning provides a powerful tool for incorporating data from related studies into a
target study of interest. In epidemiology and medical studies, the classification of a target …

We provide novel theoretical results regarding local optima of regularized M-estimators,
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …

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

A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable
Regularizers Page 1 Statistical Science 2012, Vol. 27, No. 4, 538–557 DOI: 10.1214/12-STS400 …

Optimization problems with rank constraints arise in many applications, including matrix
regression, structured PCA, matrix completion and matrix decomposition problems. An …