Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

We show that machine learning methods, in particular, extreme trees and neural networks
(NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts …

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 …

The recent advent of remote sensing, mobile technologies, novel transaction systems, and
highperformance computing offers opportunities to understand trends, behaviors, and …

We propose a new approach for data-driven automated discovery of isotropic hyperelastic
constitutive laws. The approach is unsupervised, ie, it requires no stress data but only …

In many applications, it is important to reconstruct a fluid flow field, or some other high-
dimensional state, from limited measurements and limited data. In this work, we propose a …

In this article, we introduce lassopack, a suite of programs for regularized regression in
Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive …

Data with a large number of variables relative to the sample size—“high-dimensional data”—
are readily available and increasingly common in empirical economics. Highdimensional …