Rates of Convergence for Sparse Variational Gaussian Process Regression

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Rates of Convergence for Sparse Variational Gaussian Process Regression

June 9 - 15 2019 ICML, Long Beach, USA

Authors: David Burt (University of Cambridge), Carl Edward Rasmussen (University of Cambridge) and Mark van der Wilk

Abstract: We prove that our Gaussian process approximations can work well with much less computational requirements than what was known before. Essentially, if the opposite was proved to be true, this would make large scale GP models much more computationally expensive, bringing into question whether GPs would ever be useful for large datasets.

Probabilistic Modelling

Gaussian Processes


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