Coresets for clustering with fairness constraints
L Huang, S Jiang, N Vishnoi - Advances in neural …, 2019 - proceedings.neurips.cc
In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
[PDF][PDF] Coresets for Clustering with Fairness Constraints
L Huang, HCJ Shaofeng, NK Vishnoi - arXiv preprint arXiv …, 2019 - academia.edu
Abstract In a recent work,[19] studied the following “fair” variants of classical clustering
problems such as k-means and k-median: given a set of n data points in Rd and a binary …
problems such as k-means and k-median: given a set of n data points in Rd and a binary …
Coresets for clustering with fairness constraints
L Huang, SHC Jiang, NK Vishnoi - Advances in Neural Information …, 2019 - research.aalto.fi
Abstract In a recent work,[20] studied the following “fair” variants of classical clustering
problems such as k-means and k-median: given a set of n data points in R d and a binary …
problems such as k-means and k-median: given a set of n data points in R d and a binary …
[PDF][PDF] Coresets for Clustering with Fairness Constraints
L Huang, S Jiang, NK Vishnoi - Advances in Neural Information …, 2019 - par.nsf.gov
Abstract In a recent work,[20] studied the following “fair” variants of classical clustering
problems such as k-means and k-median: given a set of n data points in Rd and a binary …
problems such as k-means and k-median: given a set of n data points in Rd and a binary …
Coresets for Clustering with Fairness Constraints
L Huang, SHC Jiang… - 33rd Conference on …, 2019 - weizmann.elsevierpure.com
In a recent work,\cite {chierichetti2017fair} studied the following fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
Coresets for Clustering with Fairness Constraints
L Huang, SHC Jiang, NK Vishnoi - arXiv preprint arXiv:1906.08484, 2019 - arxiv.org
In a recent work,[19] studied the following" fair" variants of classical clustering problems such
as $ k $-means and $ k $-median: given a set of $ n $ data points in $\mathbb {R}^ d $ and a …
as $ k $-means and $ k $-median: given a set of $ n $ data points in $\mathbb {R}^ d $ and a …
Coresets for Clustering with Fairness Constraints
L Huang, S Jiang, N Vishnoi - Advances in Neural …, 2019 - proceedings.neurips.cc
In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
Coresets for clustering with fairness constraints
L Huang, SHC Jiang, NK Vishnoi - Proceedings of the 33rd International …, 2019 - dl.acm.org
In a recent work,[20] studied the following" fair" variants of classical clustering problems such
as k-means and k-median: given a set of n data points in ℝ d and a binary type associated to …
as k-means and k-median: given a set of n data points in ℝ d and a binary type associated to …
Coresets for Clustering with Fairness Constraints
L Huang, SHC Jiang… - … 32 (NIPS 2019 …, 2019 - … .esploro.exlibrisgroup.com
In a recent work,\\cite {chierichetti2017fair} studied the following fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
Coresets for Clustering with Fairness Constraints
L Huang, SHC Jiang, NK Vishnoi - openreview.net
In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …