Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …

In this review article, the latest applications of machine learning (ML) in the additive
manufacturing (AM) field are reviewed. These applications, such as parameter optimization …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …

Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …

Most screening tests for T2DM in use today were developed using multivariate regression
methods that are often further simplified to allow transformation into a scoring formula. The …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

We consider training models with differential privacy (DP) using mini-batch gradients. The
existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD) …

An ever-increasing share of human interaction, communication, and culture is recorded as
digital text. We provide an introduction to the use of text as an input to economic research …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …