Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An $\ell^p$-based Kernel Conditional Independence Test   We propose a new computationally efficient test for conditional independence
based on the $L^{p}$ distance between two kernel-based representatives of well
suited distributions. By evaluating the difference of these two representatives
at a finite set of locations, we derive a finite dimensional approximation of
the $L^{p}$ metric, obtain its asymptotic distribution under the null
hypothesis of conditional independence and design a simple statistical test
from it. The test obtained is consistent and computationally efficient. We
conduct a series of experiments showing that the performance of our new tests
outperforms state-of-the-art methods both in term of statistical power and
type-I error even in the high dimensional setting.

28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol