Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Structured Mixtures of Gaussian Processes   Gaussian Processes (GPs) are powerful non-parametric Bayesian regression
models that allow exact posterior inference, but exhibit high computational and
memory costs. In order to improve scalability of GPs, approximate posterior
inference is frequently employed, where a prominent class of approximation
techniques is based on local GP experts. However, local-expert techniques
proposed so far are either not well-principled, come with limited approximation
guarantees, or lead to intractable models. In this paper, we introduce deep
structured mixtures of GP experts, a stochastic process model which i) allows
exact posterior inference, ii) has attractive computational and memory costs,
and iii) when used as GP approximation, captures predictive uncertainties
consistently better than previous expert-based approximations. In a variety of
experiments, we show that deep structured mixtures have a low approximation
error and often perform competitive or outperform prior work.

20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int