Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear
  Time   We study the problem of learning Bayesian networks where an
$\epsilon$-fraction of the samples are adversarially corrupted. We focus on the
fully-observable case where the underlying graph structure is known. In this
work, we present the first nearly-linear time algorithm for this problem with a
dimension-independent error guarantee. Previous robust algorithms with
comparable error guarantees are slower by at least a factor of $(d/\epsilon)$,
where $d$ is the number of variables in the Bayesian network and $\epsilon$ is
the fraction of corrupted samples.
  Our algorithm and analysis are considerably simpler than those in previous
work. We achieve this by establishing a direct connection between robust
learning of Bayesian networks and robust mean estimation. As a subroutine in
our algorithm, we develop a robust mean estimation algorithm whose runtime is
nearly-linear in the number of nonzeros in the input samples, which may be of
independent interest.

27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics