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Gaussian Processes (GPs) are highly expressive, probabilistic models.
“Metric Gaussian Variational Inference”
Jakob Knollmüller and Torsten. Ensslin · 1901
Earlier work this paper cites.
“On the convergence of a relaxation method with natural constraints on the elliptic operator”
Nikolai Bakhvalov · 1966
Earlier work this paper cites.
“An Introduction to Multigrid Methods”, An Introduction to Multigrid Methods
P. Wesseling · 2004
Earlier work this paper cites.
“Sparse Gaussian Processes using Pseudo-inputs”
Edward Snelson and Zoubin Ghahramani · 2005
Earlier work this paper cites.
“Foundations of Inference”
Kevin. Knuth and John Skilling · 2012
Earlier work this paper cites.
“Gaussian Processes for Big Data”
James Hensman, Nicolò Fusi and Neil. Lawrence · 2013
Earlier work this paper cites.
“Fast Kernel Learning for Multidimensional Pattern Extrapolation”
Andrew Wilson, Elad Gilboa, Arye Nehorai and John Cunningham · 2014
Earlier work this paper cites.
“Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)”
Andrew Wilson and Hannes Nickisch · 2015
Earlier work this paper cites.
“Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)”
Andrew Wilson and Hannes Nickisch · 2015
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Andrew Wilson, Christoph Dann and Hannes Nickisch · 2015
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“When Gaussian Process Meets Big Data: A Review of Scalable GPs”
Haitao Liu, Y. Ong, Xiaobo Shen and Jianfei Cai · 2019
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“Exact Gaussian Processes on a Million Data Points”
Ke Wang et al · 2019
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“Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization”
Geoff Pleiss et al · 2020
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“Automatic Differentiation Variational Inference with Mixtures”
Warren Morningstar et al · 2021
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“Geometric Variational Inference”
Philipp Frank, Reimar Leike and Torsten. Enßlin · 2021
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“NIFTy: Numerical Information Field Theory”, 2022
Philipp Arras et al · 2022
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“Constant-Time Predictive Distributions for Gaussian Processes”
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“Scientific Computing: An Introductory Survey, Revised Second Edition”
Michael Heath · 2018
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