Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Theory for BART   Ensemble learning is a statistical paradigm built on the premise that many
weak learners can perform exceptionally well when deployed collectively. The
BART method of Chipman et al. (2010) is a prominent example of Bayesian
ensemble learning, where each learner is a tree. Due to its impressive
performance, BART has received a lot of attention from practitioners. Despite
its wide popularity, however, theoretical studies of BART have begun emerging
only very recently. Laying the foundations for the theoretical analysis of
Bayesian forests, Rockova and van der Pas (2017) showed optimal posterior
concentration under conditionally uniform tree priors. These priors deviate
from the actual priors implemented in BART. Here, we study the exact BART prior
and propose a simple modification so that it also enjoys optimality properties.
To this end, we dive into branching process theory. We obtain tail bounds for
the distribution of total progeny under heterogeneous Galton-Watson (GW)
processes exploiting their connection to random walks. We conclude with a
result stating the optimal rate of posterior convergence for BART.

23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph