Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Greedy Step Averaging: A parameter-free stochastic optimization method   In this paper we present the greedy step averaging(GSA) method, a
parameter-free stochastic optimization algorithm for a variety of machine
learning problems. As a gradient-based optimization method, GSA makes use of
the information from the minimizer of a single sample's loss function, and
takes average strategy to calculate reasonable learning rate sequence. While
most existing gradient-based algorithms introduce an increasing number of hyper
parameters or try to make a trade-off between computational cost and
convergence rate, GSA avoids the manual tuning of learning rate and brings in
no more hyper parameters or extra cost. We perform exhaustive numerical
experiments for logistic and softmax regression to compare our method with the
other state of the art ones on 16 datasets. Results show that GSA is robust on
various scenarios.

16
The categories of this paper are: stat
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph