Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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
$\mathcal{O}(p)$ time. Our GRIEF kernel consists of $p$ eigenfunctions found
using a Nystrom approximation from a dense Cartesian product grid of inducing
points. By exploiting algebraic properties of Kronecker and Khatri-Rao tensor
products, computational complexity of the training procedure can be practically
independent of the number of inducing points. This allows us to use arbitrarily
many inducing points to achieve a globally accurate kernel approximation, even
in high-dimensional problems. The fast likelihood evaluation enables type-I or
II Bayesian inference on large-scale datasets. We benchmark our algorithms on
real-world problems with up to two-million training points and $10^{33}$
inducing points.

35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg