COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgroups. In such settings, directly extending existing fair clustering algorithms is computationally expensive or numerically unstable, especially when the number of subgroups grows exponentially and some subgroups contain only a few instances. To address these challenges, we define a subgroup-fairness gap for clustering and derive a covariance-based surrogate that exactly matches this gap. We then introduce a continuou
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- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- LinkedLinked via arxiv author · 85%Kyungseon Lee →
“COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering”
- LinkedLinked via arxiv author · 85%Hankyo Jeong →
“COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering”
- LinkedLinked via arxiv author · 85%Kunwoong Kim →
“COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering”
- LinkedLinked via arxiv author · 85%Kwanho Lee →
“COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering”
- LinkedLinked via arxiv author · 85%Yongdai Kim →
“COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering”