Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for
  Regularized Empirical Risk Minimization   In this paper, we develop a new accelerated stochastic gradient method for
efficiently solving the convex regularized empirical risk minimization problem
in mini-batch settings. The use of mini-batches is becoming a golden standard
in the machine learning community, because mini-batch settings stabilize the
gradient estimate and can easily make good use of parallel computing. The core
of our proposed method is the incorporation of our new "double acceleration"
technique and variance reduction technique. We theoretically analyze our
proposed method and show that our method much improves the mini-batch
efficiencies of previous accelerated stochastic methods, and essentially only
needs size $\sqrt{n}$ mini-batches for achieving the optimal iteration
complexities for both non-strongly and strongly convex objectives, where $n$ is
the training set size. Further, we show that even in non-mini-batch settings,
our method achieves the best known convergence rate for both non-strongly and
strongly convex objectives.

34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
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
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg