Approximation algorithms for fair range clustering
SS Hotegni, S Mahabadi… - … Conference on Machine …, 2023 - proceedings.mlr.press
This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …
different demographic groups and the goal is to pick $ k $ centers with the minimum …
Parameterized approximation schemes for clustering with general norm objectives
This paper considers the well-studied algorithmic regime of designing a (1+ϵ)-
approximation algorithm for a k-clustering problem that runs in time f(k,ϵ)poly(n) (sometimes …
approximation algorithm for a k-clustering problem that runs in time f(k,ϵ)poly(n) (sometimes …
Efficient algorithms for fair clustering with a new notion of fairness
We revisit the problem of fair clustering, first introduced by Chierichetti et al.(Fair clustering
through fairlets, 2017), which requires each protected attribute to have approximately equal …
through fairlets, 2017), which requires each protected attribute to have approximately equal …
Scalable Algorithms for Individual Preference Stable Clustering
R Mosenzon, A Vakilian - International Conference on …, 2024 - proceedings.mlr.press
In this paper, we study the individual preference (IP) stability, which is an notion capturing
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …
Parameterized approximation for robust clustering in discrete geometric spaces
We consider the well-studied Robust $(k, z) $-Clustering problem, which generalizes the
classic $ k $-Median, $ k $-Means, and $ k $-Center problems. Given a constant $ z\ge 1 …
classic $ k $-Median, $ k $-Means, and $ k $-Center problems. Given a constant $ z\ge 1 …
Parameterized Approximation Results for Clustering and Graph Packing Problems
A Gadekar - 2023 - aaltodoc.aalto.fi
The domains of clustering and graph packing have been the focus of extensive research
across multiple disciplines, including optimization, machine learning, data mining …
across multiple disciplines, including optimization, machine learning, data mining …
On Socially Fair Regression and Low-Rank Approximation
Regression and low-rank approximation are two fundamental problems that are applied
across a wealth of machine learning applications. In this paper, we study the question of …
across a wealth of machine learning applications. In this paper, we study the question of …