Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variance-Reduced and Projection-Free Stochastic Optimization   The Frank-Wolfe optimization algorithm has recently regained popularity for
machine learning applications due to its projection-free property and its
ability to handle structured constraints. However, in the stochastic learning
setting, it is still relatively understudied compared to the gradient descent
counterpart. In this work, leveraging a recent variance reduction technique, we
propose two stochastic Frank-Wolfe variants which substantially improve
previous results in terms of the number of stochastic gradient evaluations
needed to achieve $1-\epsilon$ accuracy. For example, we improve from
$O(\frac{1}{\epsilon})$ to $O(\ln\frac{1}{\epsilon})$ if the objective function
is smooth and strongly convex, and from $O(\frac{1}{\epsilon^2})$ to
$O(\frac{1}{\epsilon^{1.5}})$ if the objective function is smooth and
Lipschitz. The theoretical improvement is also observed in experiments on
real-world datasets for a multiclass classification application.

8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg