Fetching the paper…
Reading the bibliography…
We study the theoretical properties of a variational Bayes method in the Gaussian Process regression model.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
Cornelius Lanczos · 1950
Earlier work this paper cites.
Eigenvalue distribution of compact operators , volume 16
Hermann König · 1986
Earlier work this paper cites.
Small deviation probabilities of sums of independent random variables
T. Dunker, M.A. Lifshits, and W. Linde · 1998
Earlier work this paper cites.
Convergence rates of posterior distributions
Subhashis Ghosal, Jayanta K Ghosh, and Aad van der Vaart · 2000
Earlier work this paper cites.
The stability of kernel principal components analysis and its relation to the process eigenspectrum
John Shawe-Taylor and Christopher KI Williams · 2003
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
Earlier work this paper cites.
Addendum to: Information consistency of nonparametric Gaussian process methods
Matthias Seeger · 2007
Earlier work this paper cites.
Adaptive Bayesian estimation using a Gaussian random field with inverse gamma bandwidth
Aad van der Vaart and Harry van Zanten · 2009
Cited alongside, same era.
Information rates of nonparametric Gaussian process methods
Aad van der Vaart and Harry van Zanten · 2011
Cited alongside, same era.
Functional quantization-based stratified sampling methods
Sylvain Corlay and Gilles Pagès · 2015
Cited alongside, same era.
Adaptive Bayesian credible sets in regression with a Gaussian process prior
Suzanne Sniekers and Aad van der Vaart · 2015
Cited alongside, same era.
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G de G Matthews, James Hensman, Richard Turner, and Zoubin Ghahramani · 2016
Cited alongside, same era.
Fundamentals of nonparametric Bayesian inference , volume 44
Subhashis Ghosal and Aad van der Vaart · 2017
Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator
Judith Rousseau and Botond Szabo · 2017
Later among the works it cites.
Variational Fourier features for Gaussian processes
James Hensman, Nicolas Durrande, and Arno Solin · 2018
Later among the works it cites.
Rates of convergence for sparse variational Gaussian process regression
David R. Burt, Carl Edward Rasmussen, and Mark van der Wilk · 2019
Later among the works it cites.
Convergence of sparse variational inference in Gaussian processes regression
David R. Burt, Carl Edward Rasmussen, and Mark van der Wilk · 2020
Later among the works it cites.
When Gaussian process meets big data: A review of scalable GPs
Haitao Liu, Yew-Soon Ong, Xiaobo Shen, and Jianfei Cai · 2020
Later among the works it cites.
Variational Bayes for high-dimensional linear regression with sparse priors
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Variational model selection for sparse Gaussian process regression
Michalis Titsias
Cited in the paper.
Variational learning of inducing variables in sparse Gaussian processes
Michalis Titsias
Cited in the paper.
Reproducing kernel Hilbert spaces of Gaussian priors
Aad van der Vaart and Harry van Zanten
Cited in the paper.
Rates of contraction of posterior distributions based on Gaussian process priors
Aad van der Vaart and Harry van Zanten
Cited in the paper.
Kolyan Ray and Botond Szabo · 2021
Closest in time.