Fetching the paper…
Reading the bibliography…
We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based.
The Concave-Convex Procedure (CCCP)
Yuille, A. L.; and Rangarajan, A. 2001 · 2001
Earlier work this paper cites.
A Submodular-supermodular Procedure with Applications to Discriminative Structure Learning
Narasimhan, M.; and Bilmes, J. 2005 · 2005
Earlier work this paper cites.
Parameter convergence for EM and MM algorithms
Vaida, F. 2005 · 2005
Earlier work this paper cites.
Numerical optimization
Nocedal, J.; and Wright, S. 2006 · 2006
Earlier work this paper cites.
k-means++: The advantages of careful seeding
Arthur, D.; and Vassilvitskii, S. 2007 · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Von Luxburg, U. 2007 · 2007
Earlier work this paper cites.
Surrogate maximization/minimization algorithms and extensions
Zhang, Z.; Kwok, J. T.; and Yeung, D.-Y. 2007 · 2007
Earlier work this paper cites.
Information theory: coding theorems for discrete memoryless systems
Csiszar, I.; and Körner, J. 2011 · 2011
Earlier work this paper cites.
A data-driven approach to predict the success of bank telemarketing
Moro, S.; Cortez, P.; and Rita, P. 2014 · 2014
Earlier work this paper cites.
Learning deep representations for graph clustering
Tian, F.; Gao, B.; Cui, Q.; Chen, E.; and Liu, T.-Y. 2014 · 2014
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
Earlier work this paper cites.
Propublica – machine bias
Julia, A.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
Cited alongside, same era.
The Variational Nystrom method for large-scale spectral problems
Vladymyrov, M.; and Carreira-Perpiñán, M. 2016 · 2016
Cited alongside, same era.
Fair Clustering Through Fairlets
Chierichetti, F.; Kumar, R.; Lattanzi, S.; and Vassilvitskii, S. 2017 · 2017
Cited alongside, same era.
UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
Cited alongside, same era.
Human decisions and machine predictions
Kleinberg, J.; Lakkaraju, H.; Leskovec, J.; Ludwig, J.; and Mullainathan, S. 2017 · 2017
Cited alongside, same era.
Bregman-Proximal Augmented Lagrangian Approach to Multiphase Image Segmentation
Yuan, J.; Yin, K.; Bai, Y.; Feng, X.; and Tai, X. 2017 · 2017
Cited alongside, same era.
Privacy preserving clustering with constraints
Rösner, C.; and Schmidt, M. 2018 · 2018
Later among the works it cites.
The Price of Fair PCA: One Extra dimension
Samadi, S.; Tantipongpipat, U. T.; Morgenstern, J. H.; Singh, M.; and Vempala, S. 2018 · 2018
Later among the works it cites.
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering
Schmidt, M.; Schwiegelshohn, C.; and Sohler, C. 2018 · 2018
Later among the works it cites.
SpectralNet: Spectral Clustering using Deep Neural Networks
Shaham, U.; Stanton, K.; Li, H.; Basri, R.; Nadler, B.; and Kluger, Y. 2018 · 2018
Later among the works it cites.
Scalable Laplacian K-modes
Ziko, I.; Granger, E.; and Ayed, I. B. 2018 · 2018
Later among the works it cites.
Scalable fair clustering
Backurs, A.; Indyk, P.; Onak, K.; Schieber, B.; Vakilian, A.; and Wagner, T. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fairness Constraints: Mechanisms for Fair Classification
Zafar, M. B.; Valera, I.; Gomez-Rodriguez, M.; and Gummadi, K. P. 2017 · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J.; and Gebru, T. 2018 · 2018
Cited alongside, same era.
Fair and Diverse DPP-Based Data Summarization
Celis, L. E.; Keswani, V.; Straszak, D.; Deshpande, A.; Kathuria, T.; and Vishnoi, N. K. 2018 · 2018
Cited alongside, same era.
Empirical Risk Minimization Under Fairness Constraints
Donini, M.; Oneto, L.; Ben-David, S.; Shawe-Taylor, J.; and Pontil, M. 2018 · 2018
Cited alongside, same era.
Closest in time.
Fair algorithms for clustering
Bera, S.; Chakrabarty, D.; Flores, N.; and Negahbani, M. 2019 · 2019
Closest in time.
Coresets for clustering with fairness constraints
Huang, L.; Jiang, S.; and Vishnoi, N. 2019 · 2019
Closest in time.
Guarantees for Spectral Clustering with Fairness Constraints
Kleindessner, M.; Samadi, S.; Awasthi, P.; and Morgenstern, J. 2019 · 2019
Closest in time.
Kernel Cuts: Kernel and Spectral Clustering Meet Regularization
Tang, M.; Marin, D.; Ayed, I. B.; and Boykov, Y. 2019 · 2019
Closest in time.