In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

In the age of artificial intelligence, the best approach to handling huge amounts of data is a
tremendously motivating and hard problem. Among machine learning models, stochastic …

Best subset selection via a modern optimization lens Page 1 The Annals of Statistics 2016, Vol.
44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016 …

To improve the detection ability of infrared small targets in complex backgrounds, a novel
method based on non-convex rank approximation minimization joint l 2, 1 norm (NRAM) was …

Nonconvex and nonsmooth problems have recently received considerable attention in
signal/image processing, statistics and machine learning. However, solving the nonconvex …

The log-sum function as a penalty has always been drawing widespread attention in the
field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz …

Algorithms have never been more important. As the recipes of computer programs,
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

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

The nuclear norm is widely used as a convex surrogate of the rank function in compressive
sensing for low rank matrix recovery with its applications in image recovery and signal …