COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering

arXiv:2607.18119 · stat.ML, cs.LG · Submitted 2026-07-20 · Read on arXiv

Kyungseon Lee, Hankyo Jeong, Kunwoong Kim, Kwanho Lee, Yongdai Kim

stat.ML, cs.LG

Submitted: 2026-07-20

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 74× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Computational Efficiency — 7× in this paper · explained in 2 episodes
• Editor — 4× in this paper · explained in 2 episodes
• Papers — 4× in this paper · explained in 3 episodes
• COVAriance-based Fair Clustering — 3× in this paper
• K-means Clustering — 3× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Hyperparameters — 2× in this paper · explained in 2 episodes
• Institutional Review Board — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Generator — 1× in this paper · explained in 2 episodes
• KKT conditions — 1× in this paper · explained in 2 episodes
• Mutual Information — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 15 episodes
• Spectral Clustering — 1× in this paper · explained in 3 episodes

Sources

• Fair Algorithms for Clustering
• Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
• Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
• DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
• Bounding and Approximating Intersectional Fairness through Marginal Fairness

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