Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deletion Robust Submodular Maximization over Matroids   Maximizing a monotone submodular function is a fundamental task in machine
learning. In this paper, we study the deletion robust version of the problem
under the classic matroids constraint. Here the goal is to extract a small size
summary of the dataset that contains a high value independent set even after an
adversary deleted some elements. We present constant-factor approximation
algorithms, whose space complexity depends on the rank $k$ of the matroid and
the number $d$ of deleted elements. In the centralized setting we present a
$(3.582+O(\varepsilon))$-approximation algorithm with summary size $O(k +
\frac{d \log k}{\varepsilon^2})$. In the streaming setting we provide a
$(5.582+O(\varepsilon))$-approximation algorithm with summary size and memory
$O(k + \frac{d \log k}{\varepsilon^2})$. We complement our theoretical results
with an in-depth experimental analysis showing the effectiveness of our
algorithms on real-world datasets.

6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
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The categories of this paper are: eess
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys