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 continuous relaxation of the surrogate, enabling efficient gradient-based optimization and yielding our pr...
| Authors | Kyungseon Lee, Hankyo Jeong, Kunwoong Kim et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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