We establish theoretical results concerning all local optima of various regularized M-
estimators, where both loss and penalty functions are allowed to be nonconvex. Our results …

The R package partykit provides a flexible toolkit for learning, representing, summarizing,
and visualizing a wide range of tree-structured regression and classification models. The …

The underlying representations of many real-world processes are often sparse. For
example, in disease diagnosis, even though humans have a large number of genes, only a …

The group Lasso is an extension of the Lasso for feature selection on (predefined) non-
overlapping groups of features. The non-overlapping group structure limits its applicability in …

We introduce a novel family of invariant, convex, and non-quadratic functionals that we
employ to derive regularized solutions of ill-posed linear inverse imaging problems. The …

Multimodality based methods have shown great advantages in classification of Alzheimer's
disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently …

In this paper, we present a new adaptive feature scaling scheme for ultrahigh-dimensional
feature selection on Big Data, and then reformulate it as a convex semi-infinite programming …

The resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool that can
reveal brain dysfunction in the computer-aided diagnosis of the autism spectrum disorder …

Data missing remains a difficult and important problem in the transportation information
system, which seriously restricts the application of the intelligent transportation system (ITS) …

We introduce a novel generic energy functional that we employ to solve inverse imaging
problems within a variational framework. The proposed regularization family, termed as …