Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic dual averaging methods using variance reduction techniques
  for regularized empirical risk minimization problems   We consider a composite convex minimization problem associated with
regularized empirical risk minimization, which often arises in machine
learning. We propose two new stochastic gradient methods that are based on
stochastic dual averaging method with variance reduction. Our methods generate
a sparser solution than the existing methods because we do not need to take the
average of the history of the solutions. This is favorable in terms of both
interpretability and generalization. Moreover, our methods have theoretical
support for both a strongly and a non-strongly convex regularizer and achieve
the best known convergence rates among existing nonaccelerated stochastic
gradient methods.

11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph