Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm   Sample- and computationally-efficient distribution estimation is a
fundamental tenet in statistics and machine learning. We present SURF, an
algorithm for approximating distributions by piecewise polynomials. SURF is:
simple, replacing prior complex optimization techniques by straight-forward
{empirical probability} approximation of each potential polynomial piece
{through simple empirical-probability interpolation}, and using plain
divide-and-conquer to merge the pieces; universal, as well-known
polynomial-approximation results imply that it accurately approximates a large
class of common distributions; robust to distribution mis-specification as for
any degree $d \le 8$, it estimates any distribution to an $\ell_1$ distance $<
3$ times that of the nearest degree-$d$ piecewise polynomial, improving known
factor upper bounds of 3 for single polynomials and 15 for polynomials with
arbitrarily many pieces; fast, using optimal sample complexity, running in near
sample-linear time, and if given sorted samples it may be parallelized to run
in sub-linear time. In experiments, SURF outperforms state-of-the art
algorithms.

12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th