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We study the fair variant of the classic $k$-median problem introduced by Chierichetti et al.
Probabilistic approximation of metric spaces and its algorithmic applications
Y. Bartal · 1996
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Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid
R. Kohavi · 1996
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Algorithmic applications of low-distortion geometric embeddings
P. Indyk · 2001
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A data-driven approach to predict the success of bank telemarketing
S. Moro, P. Cortez, and P. Rita · 2014
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B. Strack, J. P. DeShazo, C. Gennings, J. L. Olmo, S. Ventura, K. J. Cios, and J. N. Clore · 2014
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Dimensionality reduction for k k -means clustering and low rank approximation
M. B. Cohen, S. Elder, C. Musco, C. Musco, and M. Persu · 2015
Cited alongside, same era.
Fair clustering through fairlets
F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
D. Dheeru and E. Karra Taniskidou · 2017
Cited alongside, same era.
On the cost of essentially fair clusterings
I. O. Bercea, M. Groß, S. Khuller, A. Kumar, C. Rösner, D. R. Schmidt, and M. Schmidt · 2018
Cited alongside, same era.
The frontiers of fairness in machine learning
A. Chouldechova and A. Roth · 2018
Later among the works it cites.
Performance of johnson-lindenstrauss transform for k k -means and k k -medians clustering
K. Makarychev, Y. Makarychev, and I. Razenshteyn · 2018
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Privacy preserving clustering with constraints
C. Rösner and M. Schmidt · 2018
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Fair coresets and streaming algorithms for fair k k -means clustering
M. Schmidt, C. Schwiegelshohn, and C. Sohler · 2018
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Fair algorithms for clustering
S. K. Bera, D. Chakrabarty, and M. Negahbani · 2019
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