Variational Gaussian Processes: A Functional Analysis View
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Abstract (arXiv)
Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficiency. So far the common choices in the literature are disparate and lacking generality. We propose to view the GP as lying in a Banach space which then facilitates a unified perspective. This is used to understand the relationship between existing features and to draw a connection between kernel ridge regression and variational GP approximations.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 25 Oct 2021.
Builds on
14 of 48 references.
References · 14
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
- 2013Gaussian Processes for Big Datacited by 383
- 2018Gaussian Processes and Kernel Methods: A Review on Connections and Equivalencescited by 192
- 2019Exact Gaussian Processes on a Million Data Pointscited by 119
- 2015Less is More: Nystr"om Computational Regularizationcited by 91
- 2016Variational Fourier features for Gaussian processescited by 85
- 2015On Sparse variational methods and the Kullback-Leibler divergence between stochastic processescited by 57
- 2020Sparse Gaussian Processes with Spherical Harmonic Featurescited by 43
- 2020A Tutorial on Sparse Gaussian Processes and Variational Inferencecited by 31
- 2021SigGPDE: Scaling Sparse Gaussian Processes on Sequential Datacited by 22
- 2017Variational Inference for Gaussian Process Models with Linear Complexitycited by 21
- 2018Orthogonally Decoupled Variational Gaussian Processescited by 19
Built on by
Citing papers per year: 2022: 2; 2023: 1.
3 papers.