Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low

We study a seemingly unexpected and relatively less understood overfitting aspect of a fundamental tool in sparse linear modeling—best subset selection—which minimizes the residual sum of squar…

Frank--Wolfe Methods with an Unbounded Feasible Region and Applications to St…

The Frank--Wolfe method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank--Wolfe method can on…

Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Spars…

Principal component analysis (PCA) is one of the most widely used dimensionality reduction tools in scientific data analysis. The PCA direction, given by the leading eigenvector of a covariance mat…

Randomized Gradient Boosting Machine

Fast Best Subset Selection : Coordinate Descent and Local Combinatorial Optim…

A Computational Framework for Multivariate Convex Regression and Its Variants

An Extended Frank-Wolfe Method with "In-face" Directions, and its Application…