Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed High-dimensional Regression Under a Quantile Loss Function   This paper studies distributed estimation and support recovery for
high-dimensional linear regression model with heavy-tailed noise. To deal with
heavy-tailed noise whose variance can be infinite, we adopt the quantile
regression loss function instead of the commonly used squared loss. However,
the non-smooth quantile loss poses new challenges to high-dimensional
distributed estimation in both computation and theoretical development. To
address the challenge, we transform the response variable and establish a new
connection between quantile regression and ordinary linear regression. Then, we
provide a distributed estimator that is both computationally and
communicationally efficient, where only the gradient information is
communicated at each iteration. Theoretically, we show that, after a constant
number of iterations, the proposed estimator achieves a near-oracle convergence
rate without any restriction on the number of machines. Moreover, we establish
the theoretical guarantee for the support recovery. The simulation analysis is
provided to demonstrate the effectiveness of our method.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci