Research map / Learning theory and algorithms / Clustering
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · MNIST-USPS | Fβ: 75.0 -> 92.0 | Helped |
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · Color Reverse MNIST | Fβ: 81.3 -> 92.7 | Helped |
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · HAR | Fβ: 79.3 -> 89.0 | Helped |
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · Office-31 | Fβ: 77.8 -> 79.7 | Helped |
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · MTFL | Fβ: 30.0 -> 28.5 | Hurt |
| Use FCMI, jointly optimizing clustering and fairness objectives | fair clustering · Mouse Atlas | Fβ: 71.7 -> 75.0 | Helped |
| Add the fairness loss L_fair (ablation) | FCMI · MNIST-USPS | MNCE: 77.9 -> 92.3 | Helped |
| Add the fairness loss L_fair (ablation) | FCMI · MNIST-USPS | Fβ: 83.5 -> 92.0 | Helped |
| Add the clustering loss L_clu (ablation) | FCMI · MNIST-USPS | NMI: 79.2 -> 91.8 | Helped |
| Add the clustering loss L_clu (ablation) | FCMI · MNIST-USPS | Fβ: 81.1 -> 92.0 | Helped |
Closest papers by meaning
- Fair Clustering via Alignment (ICML 2025 · similarity 0.89)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.87)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.87)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.87)
- Relative Error Fair Clustering in the Weak-Strong Oracle Model (ICML 2025 · similarity 0.86)
- Fair Clustering in the Sliding Window Model (ICLR 2025 · similarity 0.86)
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