arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.
By Atticus Rex, Elizabeth Qian, David Peterson
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
By Javier Nieto-Centenero, Esther Andr\'es, Rodrigo Castellanos
arXiv:2306. 14851v5 Announce Type: replace-cross Abstract: Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner.
By Ryan Cory-Wright, Andr\'es G\'omez
arXiv:2603.05575v2 Announce Type: replace-cross
Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a blac...
By Yang Sui, Jin Zhou, Hua Zhou, Xiaowu Dai
arXiv:2510. 22491v3 Announce Type: replace Abstract: Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution.
By Ghadi Nehme, Yanxia Zhang, Dule Shu, Matt Klenk, Faez Ahmed
arXiv:2510.02532v2 Announce Type: replace-cross
Abstract: Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimensio...
By Shuo Huang, Hippolyte Labarri\`ere, Ernesto De Vito, Tomaso Poggio, Lorenzo Rosasco