Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

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 …

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

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 …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

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

Canonical correlation analysis is a classical technique for exploring the relationship
between two sets of variables. It has important applications in analyzing high dimensional …

The fused least absolute shrinkage and selection operator (LASSO) simultaneously
pursuing the joint sparsity of coefficients and their successive differences has attracted …

Many regularization schemes for high-dimensional regression have been put forward. Most
require the choice of a tuning parameter, using model selection criteria or cross-validation …