ICML 2019Oral
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