Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Faster Projection-free Online Learning   In many online learning problems the computational bottleneck for
gradient-based methods is the projection operation. For this reason, in many
problems the most efficient algorithms are based on the Frank-Wolfe method,
which replaces projections by linear optimization. In the general case,
however, online projection-free methods require more iterations than
projection-based methods: the best known regret bound scales as $T^{3/4}$.
Despite significant work on various variants of the Frank-Wolfe method, this
bound has remained unchanged for a decade. In this paper we give an efficient
projection-free algorithm that guarantees $T^{2/3}$ regret for general online
convex optimization with smooth cost functions and one linear optimization
computation per iteration. As opposed to previous Frank-Wolfe approaches, our
algorithm is derived using the Follow-the-Perturbed-Leader method and is
analyzed using an online primal-dual framework.

15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph