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We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context.
The core of an n person game
H. E. Scarf · 1967
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Lindahl’s solution and the core of an economy with public goods
D. K. Foley · 1970
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Clustering to minimize the maximum intercluster distance
T. F. Gonzalez · 1985
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Approximation algorithms for facility location problems
D. B. Shmoys, E. Tardos, and K. Aardal · 1997
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Primal-dual approximation algorithms for metric facility location and k-median problems
K. Jain and V. V. Vazirani · 1999
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A constant-factor approximation algorithm for the k-median problem
M. Charikar, S. Guha, Éva Tardos, and D. B. Shmoys · 2002
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A new greedy approach for facility location problems
K. Jain, M. Mahdian, and A. Saberi · 2002
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Local search heuristics for k-median and facility location problems
V. Arya, N. Garg, R. Khandekar, A. Meyerson, K. Munagala, and V. Pandit · 2004
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Optimal time bounds for approximate clustering
R. R. Mettu and C. G. Plaxton · 2004
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K-means++: The advantages of careful seeding
D. Arthur and S. Vassilvitskii · 2007
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Data clustering: 50 years beyond k-means
A. K. Jain · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai · 2016
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The core of the participatory budgeting problem
B. Fain, A. Goel, and K. Munagala · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, , and N. Srebro · 2016
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Machine bias, 2016
S. M. Julia Angwin, Jeff Larson and P. Lauren Kirchner · 2016
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Inherent Trade-Offs in the Fair Determination of Risk Scores
J. Kleinberg, S. Mullainathan, and M. Raghavan · 2016
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Justified representation in approval-based committee voting
H. Aziz, M. Brill, V. Conitzer, E. Elkind, R. Freeman, and T. Walsh · 2017
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Human decisions and machine predictions
J. Kleinberg, H. Lakkaraju, J. Leskovec, J. Ludwig, and S. Mullainathan · 2017
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On fairness and calibration
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 2017
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From parity to preference-based notions of fairness in classification
M. B. Zafar, I. Valera, M. Rodriguez, K. Gummadi, and A. Weller · 2017
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Fair allocation of indivisible public goods
B. Fain, K. Munagala, and N. Shah · 2018
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An improved approximation for k-median and positive correlation in budgeted optimization
J. Byrka, T. Pensyl, B. Rybicki, A. Srinivasan, and K. Trinh · 2017
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Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. J. Bryson, and A. Narayanan · 2017
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Fair clustering through fairlets
F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii · 2017
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Fair public decision making
V. Conitzer, R. Freeman, and N. Shah · 2017
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Algorithmic decision making and the cost of fairness
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq · 2017
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UCI machine learning repository, 2017
D. Dheeru and E. Karra Taniskidou · 2017
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N. Garg, A. Goel, and B. Plaut · 2018
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Non-discriminatory machine learning through convex fairness criteria
N. Goel, M. Yaghini, and B. Faltings · 2018
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Fairness without demographics in repeated loss minimization
T. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
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Privacy Preserving Clustering with Constraints
C. Rösner and M. Schmidt · 2018
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Fair Algorithms for Clustering
S. K. Bera, D. Chakrabarty, and M. Negahbani · 2019
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