Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)

We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in O(p) time. Our GRIEF kernel consists of p eigenfunctions found using a Nystrom approximation from a dense Cartesian product grid of inducing points... (read more)

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Gaussian Process
Non-Parametric Classification