Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various
challenging machine learning problems such as image recognition. Although these methods …

In this paper, we study the implicit regularization of the gradient descent algorithm in
homogeneous neural networks, including fully-connected and convolutional neural …

We study the bias of generic optimization methods, including Mirror Descent, Natural
Gradient Descent and Steepest Descent with respect to different potentials and norms, when …

Large labeled training sets are the critical building blocks of supervised learning methods
and are key enablers of deep learning techniques. For some applications, creating labeled …