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We give a local search based algorithm for $k$-median and $k$-means (and more generally for any $k$-clustering with $\ell_p$ norm cost function) from the perspective of individual fairness.
Easy and hard bottleneck location problems
Wen-Lian Hsu and George L Nemhauser · 1979
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Extensions of lipschitz mappings into a hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Clustering to minimize the maximum intercluster distance
Teofilo F Gonzalez · 1985
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A best possible heuristic for the k k -center problem
Dorit S Hochbaum and David B Shmoys · 1985
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Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications
Jorge Galindo and Pablo Tamayo · 2000
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A constant-factor approximation algorithm for the k-median problem
Moses Charikar, Sudipto Guha, Éva Tardos, and David B Shmoys · 2002
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Local search heuristics for k k -median and facility location problems
Vijay Arya, Naveen Garg, Rohit Khandekar, Adam Meyerson, Kamesh Munagala, and Vinayaka Pandit · 2004
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A local search approximation algorithm for k k -means clustering
Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu · 2004
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Spanners with slack
T-H Hubert Chan, Michael Dinitz, and Anupam Gupta · 2006
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Simpler analyses of local search algorithms for facility location
Anupam Gupta and Kanat Tangwongsan · 2008
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Local global tradeoffs in metric embeddings
Moses Charikar, Konstantin Makarychev, and Yury Makarychev · 2010
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Data Clustering: Algorithms and Applications
Charu C. Aggarwal and Chandan K. Reddy, editors · 2014
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An improved approximation for k-median, and positive correlation in budgeted optimization
Jarosław Byrka, Thomas Pensyl, Bartosz Rybicki, Aravind Srinivasan, and Khoa Trinh · 2014
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Dimensionality reduction for k k -means clustering and low rank approximation
Michael B Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu · 2015
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
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Approximating k k -median via pseudo-approximation
Shi Li and Ola Svensson · 2016
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Better guarantees for k k -means and euclidean k k -median by primal-dual algorithms
Sara Ahmadian, Ashkan Norouzi-Fard, Ola Svensson, and Justin Ward · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Fair clustering through fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii · 2017
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On the cost of essentially fair clusterings
Ioana O Bercea, Martin Groß, Samir Khuller, Aounon Kumar, Clemens Rösner, Daniel R Schmidt, and Melanie Schmidt · 2019
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Scalable fair clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner · 2019
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Sina Baharlouei, Maher Nouiehed, and Meisam Razaviyayn · 2019
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Proportionally fair clustering
Xingyu Chen, Brandon Fain, Liang Lyu, and Kamesh Munagala · 2019
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Making existing clusterings fairer: Algorithms, complexity results and insights
Ian Davidson and SS Ravi · 2019
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Fair algorithms for learning in allocation problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, and Zachary Schutzman · 2019
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UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
Cited alongside, same era.
Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
Cited alongside, same era.
Privacy preserving clustering with constraints
Clemens Rösner and Melanie Schmidt · 2018
Cited alongside, same era.
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Coresets for clustering with fairness constraints
Lingxiao Huang, Shaofeng Jiang, and Nisheeth Vishnoi · 2019
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Near neighbor: Who is the fairest of them all?
Sariel Har-Peled and Sepideh Mahabadi · 2019
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A center in your neighborhood: Fairness in facility location
Christopher Jung, Sampath Kannan, and Neil Lutz · 2019
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Fair k k -center clustering for data summarization
Matthäus Kleindessner, Pranjal Awasthi, and Jamie Morgenstern · 2019
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Guarantees for spectral clustering with fairness constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern · 2019
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Performance of johnson-lindenstrauss transform for k k -means and k k -medians clustering
Konstantin Makarychev, Yury Makarychev, and Ilya Razenshteyn · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Fair coresets and streaming algorithms for fair k k -means
Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler · 2019
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Algorithmic fairness, 2020
Dana Pessach and Erez Shmueli · 2020
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