In two, we recognized that many machine learning problems involve minimizing empirical risk functions with well-behaved population risks. Instead of analyzing the non-convex empirical risk directly, ...

A machine learning algorithm, on the other hand, might recognize that the strongest signal differentiating a dog from a cat is whether the photo is a bright outdoor photo or a dim indoor photo.

Optimization and statistics are everywhere, touching all engineering disciplines in an ever more sophisticated way. Nowhere are they more important than in the rapidly evolving field of machine ...

1. Mathematical optimization and machine learning are two highly sophisticated advanced analytics software technologies that are used in a vast array of applications, making it hard to swiftly and ...

Proximal algorithms are useful for obtaining solutions to difficult optimization problems, especially those involving nonsmooth or composite objective functions. A proximal algorithm is one whose ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine ...

This Month Published: 03 April 2018 Points of Significance Statistics versus machine learning Danilo Bzdok, Naomi Altman & Martin Krzywinski Nature Methods 15, 233–234 (2018) Cite this article ...