SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
From MaRDI portal
Publication:6336177
arXiv2003.02658MaRDI QIDQ6336177
Author name not available (Why is that?)
Publication date: 5 March 2020
Abstract: Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations. However, vanilla GP methods scale cubically in the amount of observations. In this work, we propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features. We then prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior. To furthermore illustrate the practical applicability of our method, we then apply it to ODIN, a recently developed algorithm for ODE parameter inference. In an extensive experiments section, all results are empirically validated, demonstrating the speed, accuracy, and practical applicability of this approach.
Has companion code repository: https://github.com/sdi1100041/SLEIPNIR
This page was built for publication: SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6336177)
