Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Algorithms For Fair Clustering with a New Fairness Notion   We revisit the problem of fair clustering, first introduced by Chierichetti
et al., that requires each protected attribute to have approximately equal
representation in every cluster; i.e., a balance property. Existing solutions
to fair clustering are either not scalable or do not achieve an optimal
trade-off between clustering objective and fairness. In this paper, we propose
a new notion of fairness, which we call $tau$-fair fairness, that strictly
generalizes the balance property and enables a fine-grained efficiency vs.
fairness trade-off. Furthermore, we show that simple greedy round-robin based
algorithms achieve this trade-off efficiently. Under a more general setting of
multi-valued protected attributes, we rigorously analyze the theoretical
properties of the our algorithms. Our experimental results suggest that the
proposed solution outperforms all the state-of-the-art algorithms and works
exceptionally well even for a large number of clusters.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph