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Found 4 papers, 3 papers with code
Improved Approximation for Fair Correlation Clustering
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge.
Better Algorithms for Individually Fair $k$-Clustering
In this paper, our main contribution is to use Linear Programming (LP) techniques to obtain better algorithms for this problem, both in theory and in practice.
Revisiting Priority $k$-Center: Fairness and Outliers
In the Priority $k$-Center problem, the input consists of a metric space $(X, d)$, an integer $k$, and for each point $v \in X$ a priority radius $r(v)$.
Fair Algorithms for Clustering
- We allow the user to specify the parameters that define fair representation.
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