Scaling Gaussian Process Regression with Derivatives

Cubic convolution interpolation for digital image processing

Robert Keys · 1981

Earlier work this paper cites.

Image reconstruction by convolution with symmetrical piecewise n n th-order polynomial kernels

Erik H. W. Meijering, Karel J. Zuiderveld, and Max A. Viergever · 1999

Earlier work this paper cites.

Occam’s razor

Carl Edward Rasmussen and Zoubin Ghahramani · 2001

Earlier work this paper cites.

Mount Saint Helens LiDAR data

Puget Sound LiDAR Consortium · 2002

Earlier work this paper cites.

Information theory, inference and learning algorithms

David J. C. MacKay · 2003

Earlier work this paper cites.

A unifying view of sparse approximate Gaussian process regression

Joaquin Quiñonero-Candela and Carl Edward Rasmussen · 2005

Earlier work this paper cites.

Sparse Gaussian processes using pseudo-inputs

Edward Snelson and Zoubin Ghahramani · 2005

Earlier work this paper cites.

Gaussian processes for machine learning

C. E. Rasmussen and C. K. I. Williams · 2006

Earlier work this paper cites.

Modeling data from computer experiments: an empirical comparison of kriging with MARS and projection pursuit regression

Einat Neumann Ben-Ari and David M Steinberg · 2007

Earlier work this paper cites.

An estimator for the diagonal of a matrix

Costas Bekas, Efi Kokiopoulou, and Yousef Saad · 2007

Earlier work this paper cites.

Engineering design via surrogate modelling: a practical guide

Alexander Forrester, Andy Keane, et al · 2008

Cited alongside, same era.

Rough terrain reconstruction for rover motion planning

David Gingras, Tom Lamarche, Jean-Luc Bedwani, and Érick Dupuis · 2010

Cited alongside, same era.

Large-scale visual odometry for rough terrain

Kurt Konolige, Motilal Agrawal, and Joan Sola · 2010

Cited alongside, same era.

Space-carving kernels for accurate rough terrain estimation

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, and Martial Hebert · 2010

Cited alongside, same era.

Hermite radial basis functions implicits

Ives Macedo, Joao Paulo Gois, and Luiz Velho · 2011

Cited alongside, same era.

On the low-rank approximation by the pivoted Cholesky decomposition

Helmut Harbrecht, Michael Peters, and Reinhold Schneider · 2012

Cited alongside, same era.

Kernel interpolation for scalable structured Gaussian processes (KISS-GP)

Andrew G. Wilson and Hannes Nickisch · 2015

Later among the works it cites.

Large-scale log-determinant computation through stochastic Chebyshev expansions

Insu Han, Dmitry Malioutov, and Jinwoo Shin · 2015

Later among the works it cites.

Active subspaces: Emerging ideas for dimension reduction in parameter studies

Paul G. Constantine · 2015

Later among the works it cites.

Preconditioning kernel matrices

Kurt Cutajar, Michael Osborne, John Cunningham, and Maurizio Filippone · 2016

Later among the works it cites.

Bayesian optimization with gradients

Jian Wu, Matthias Poloczek, Andrew G Wilson, and Peter Frazier · 2017

Later among the works it cites.

Scalable log determinants for Gaussian process kernel learning

Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, and Andrew G. Wilson · 2017