In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

Theory of Classification: a Survey of Some Recent Advances Page 1 ESAIM: PS ESAIM:
Probability and Statistics November 2005, Vol. 9, p. 323–375 DOI: 10.1051/ps:2005018 …

We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

During the past decade there has been an explosion in computation and information
technology. With it have come vast amounts of data in a variety of fields such as medicine …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Boosting Boosting—(is an approach to machine learning based on the idea of creating a
highly accurate prediction rule by combining many relatively weak and inaccurate rules. The …

The term “boosting” denotes a powerful means of facilitating machine learning that was
invented by the book's authors 20 years ago and intensively developed since. Despite this …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

Many of the classification algorithms developed in the machine learning literature, including
the support vector machine and boosting, can be viewed as minimum contrast methods that …