SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

Angelis Emmanouil
Angelis Emmanouil
Wenk Philippe
Wenk Philippe
Bauer Stefan
Bauer Stefan
Cited by: 0|Bibtex|Views18
Other Links: arxiv.org

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 ...More

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