Oct 29, 2018 · Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, ...

Scaling derivatives by a natural length scale gives the multi-output GP consistent units, and lets us understand approximation error without weighted norms. As ...

I Apply structured kernel interpolation to enable fast MVMs for K∇. I Combine iterative methods and stochastic estimators to avoid direct.

In this paper, we introduce methods to achieve fully scalable Gaussian process regression with derivatives using variational inference.

Jul 8, 2021 · In this paper, we introduce methods to achieve fully scalable Gaussian process regression with derivatives using variational inference.

Oct 29, 2018 · Scaling derivatives by a natural length scale gives the multi-output GP consistent units, and lets us understand approximation error without.

This repository contains code necessary to not only reproduce the experiments presented in Scalable Gaussian Process Regression with Derivatives @ NIPS 2018

This paper introduces methods to achieve fully scalable Gaussian process regression with derivatives using variational inference and introduces the concept ...

Dec 3, 2018 · Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, ...

Apr 28, 2024 · Scale the function observations and derivatives separately, but that also gives worse results for both 9D and 12D.