Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Block-diagonal Approximation Methods for Regularized
  Empirical Risk Minimization   In recent years, there is a growing need to train machine learning models on
a huge volume of data. Designing efficient distributed optimization algorithms
for empirical risk minimization (ERM) has therefore become an active and
challenging research topic. In this paper, we propose a flexible framework for
distributed ERM training through solving the dual problem, which provides a
unified description and comparison of existing methods. Our approach requires
only approximate solutions of the sub-problems involved in the optimization
process, and is versatile to be applied on many large-scale machine learning
problems including classification, regression, and structured prediction. We
show that our approach enjoys global linear convergence for a broader class of
problems, and achieves faster empirical performance, compared with existing
works.

7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys