Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scaling Gaussian Process Regression with Derivatives   Gaussian processes (GPs) with derivatives are useful in many applications,
including Bayesian optimization, implicit surface reconstruction, and terrain
reconstruction. Fitting a GP to function values and derivatives at $n$ points
in $d$ dimensions requires linear solves and log determinants with an ${n(d+1)
\times n(d+1)}$ positive definite matrix -- leading to prohibitive
$\mathcal{O}(n^3d^3)$ computations for standard direct methods. We propose
iterative solvers using fast $\mathcal{O}(nd)$ matrix-vector multiplications
(MVMs), together with pivoted Cholesky preconditioning that cuts the iterations
to convergence by several orders of magnitude, allowing for fast kernel
learning and prediction. Our approaches, together with dimensionality
reduction, enables Bayesian optimization with derivatives to scale to
high-dimensional problems and large evaluation budgets.

20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin