Rates of Convergence for Sparse Variational Gaussian Process Regression

作者: David Burt, Carl Edward Rasmussen, Mark van der Wilk

摘要

Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ the number of

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