Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Continuous Submodular Maximization: From Full-Information to
  Bandit Feedback   In this paper, we propose three online algorithms for submodular
maximisation. The first one, Mono-Frank-Wolfe, reduces the number of
per-function gradient evaluations from $T^{1/2}$ [Chen2018Online] and $T^{3/2}$
[chen2018projection] to 1, and achieves a $(1-1/e)$-regret bound of
$O(T^{4/5})$. The second one, Bandit-Frank-Wolfe, is the first bandit algorithm
for continuous DR-submodular maximization, which achieves a $(1-1/e)$-regret
bound of $O(T^{8/9})$. Finally, we extend Bandit-Frank-Wolfe to a bandit
algorithm for discrete submodular maximization, Responsive-Frank-Wolfe, which
attains a $(1-1/e)$-regret bound of $O(T^{8/9})$ in the responsive bandit
setting.

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The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat