A review of theoretical concepts and diverse applications of sparse representations and
compressive sampling (CS) approaches in engineering mechanics problems is provided …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

We add a set of convex constraints to the lasso to produce sparse interaction models that
honor the hierarchy restriction that an interaction only be included in a model if one or both …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. While naturally cast as a combinatorial optimization problem, variable or …

Sparse models are particularly useful in scientific applications, such as biomarker discovery
in genetic or neuroimaging data, where the interpretability of a predictive model is essential …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

Encoding models provide a powerful framework to identify the information represented in
brain recordings. In this framework, a stimulus representation is expressed within a feature …

We consider the problem of estimating high-dimensional Gaussian graphical models
corresponding to a single set of variables under several distinct conditions. This problem is …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …

We propose a new class of structured Schatten norms for tensors that includes two recently
proposed norms (overlapped''and" latent'') for convex-optimization-based tensor …