Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diverse Data Selection under Fairness Constraints   Diversity is an important principle in data selection and summarization,
facility location, and recommendation systems. Our work focuses on maximizing
diversity in data selection, while offering fairness guarantees. In particular,
we offer the first study that augments the Max-Min diversification objective
with fairness constraints. More specifically, given a universe $U$ of $n$
elements that can be partitioned into $m$ disjoint groups, we aim to retrieve a
$k$-sized subset that maximizes the pairwise minimum distance within the set
(diversity) and contains a pre-specified $k_i$ number of elements from each
group $i$ (fairness). We show that this problem is NP-complete even in metric
spaces, and we propose three novel algorithms, linear in $n$, that provide
strong theoretical approximation guarantees for different values of $m$ and
$k$. Finally, we extend our algorithms and analysis to the case where groups
can be overlapping.

6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an