Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Optimal One Pass Large Scale Learning with Averaged Stochastic
  Gradient Descent   For large scale learning problems, it is desirable if we can obtain the
optimal model parameters by going through the data in only one pass. Polyak and
Juditsky (1992) showed that asymptotically the test performance of the simple
average of the parameters obtained by stochastic gradient descent (SGD) is as
good as that of the parameters which minimize the empirical cost. However, to
our knowledge, despite its optimal asymptotic convergence rate, averaged SGD
(ASGD) received little attention in recent research on large scale learning.
One possible reason is that it may take a prohibitively large number of
training samples for ASGD to reach its asymptotic region for most real
problems. In this paper, we present a finite sample analysis for the method of
Polyak and Juditsky (1992). Our analysis shows that it indeed usually takes a
huge number of samples for ASGD to reach its asymptotic region for improperly
chosen learning rate. More importantly, based on our analysis, we propose a
simple way to properly set learning rate so that it takes a reasonable amount
of data for ASGD to reach its asymptotic region. We compare ASGD using our
proposed learning rate with other well known algorithms for training large
scale linear classifiers. The experiments clearly show the superiority of ASGD.

31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph