Understanding Kernel Ridge Regression: Common behaviors from simple
functions to density functionals

v1v2 (latest)

Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals

16 January 2015

Brandon F. Chen

ArXiv (abs)PDF HTML

Papers citing "Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals"

7 / 7 papers shown

Title
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Sergei Manzhos Manabu Ihara 99 8 0 17 Nov 2023
Multi-Fidelity Machine Learning for Excited State Energies of Molecules Vivin Vinod Sayan Maity Peter Zaspel Ulrich Kleinekathöfer AI4CE 70 9 0 18 May 2023
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces Sergei Manzhos Manabu Ihara GP 23 6 0 21 Nov 2022
Model inference for Ordinary Differential Equations by parametric polynomial kernel regression David K. E. Green F. Rindler 33 2 0 06 Aug 2019
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment John E. Herr Kun Yao R. McIntyre David W Toth John A. Parkhill 61 63 0 19 Dec 2017
By-passing the Kohn-Sham equations with machine learning Felix Brockherde Leslie Vogt Li Li M. Tuckerman K. Burke K. Müller AI4CE 121 607 0 09 Sep 2016
Understanding Machine-learned Density Functionals Li Li John C. Snyder I. Pelaschier Jessica Huang U. Niranjan Paul Duncan M. Rupp K. Müller K. Burke 116 152 0 04 Apr 2014