Fair algorithms for clustering
S Bera, D Chakrabarty, N Flores… - Advances in Neural …, 2019 - proceedings.neurips.cc
We study the problem of finding low-cost {\em fair clusterings} in data where each data point
may belong to many protected groups. Our work significantly generalizes the seminal work …
may belong to many protected groups. Our work significantly generalizes the seminal work …
Better Guarantees for -Means and Euclidean -Median by Primal-Dual Algorithms
Clustering is a classic topic in optimization with k-means being one of the most fundamental
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …
Proportionally fair clustering
We extend the fair machine learning literature by considering the problem of proportional
centroid clustering in a metric context. For clustering n points with k centers, we define …
centroid clustering in a metric context. For clustering n points with k centers, we define …
Towards optimal lower bounds for k-median and k-means coresets
V Cohen-Addad, KG Larsen, D Saulpic… - Proceedings of the 54th …, 2022 - dl.acm.org
The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …
sum of distances raised to the power of z of every data point to its closest center is …
On the cost of essentially fair clusterings
Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and
may be used to make decisions for each point based on its group. However, this process …
may be used to make decisions for each point based on its group. However, this process …
Privacy preserving clustering with constraints
C Rösner, M Schmidt - arxiv preprint arxiv:1802.02497, 2018 - arxiv.org
The $ k $-center problem is a classical combinatorial optimization problem which asks to
find $ k $ centers such that the maximum distance of any input point in a set $ P $ to its …
find $ k $ centers such that the maximum distance of any input point in a set $ P $ to its …
The hardness of approximation of euclidean k-means
The Euclidean $ k $-means problem is a classical problem that has been extensively
studied in the theoretical computer science, machine learning and the computational …
studied in the theoretical computer science, machine learning and the computational …
Local Search Yields Approximation Schemes for -Means and -Median in Euclidean and Minor-Free Metrics
V Cohen-Addad, PN Klein, C Mathieu - SIAM Journal on Computing, 2019 - SIAM
We give the first polynomial-time approximation schemes (PTASs) for the following
problems:(1) uniform facility location in edge-weighted planar graphs;(2) k-median and k …
problems:(1) uniform facility location in edge-weighted planar graphs;(2) k-median and k …
Improved approximations for Euclidean k-means and k-median, via nested quasi-independent sets
Motivated by data analysis and machine learning applications, we consider the popular high-
dimensional Euclidean k-median and k-means problems. We propose a new primal-dual …
dimensional Euclidean k-median and k-means problems. We propose a new primal-dual …
Individual fairness for k-clustering
S Mahabadi, A Vakilian - International conference on …, 2020 - proceedings.mlr.press
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 …
generally for any $ k $-clustering with $\ell_p $ norm cost function) from the perspective of …