Research map / Learning theory and algorithms / Fairness
On Learning Fairness and Accuracy on Multiple Subgroups
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use bilevel FAMS instead of ERM | DistilBERT MLP · Amazon review | group sufficiency gap: 3.0% improvement | Helped |
| use bilevel FAMS instead of ERM | DistilBERT MLP · Amazon review | accuracy | Hurt |
| use FAMS instead of baselines | DistilBERT MLP · Toxic comments (race) | group sufficiency gap: 3.0-3.5% improvement | Helped |
| use FAMS instead of ERM | 2-layer MLP · Adult | group sufficiency gap: 1.096 -> 0.684 | Helped |
| use FAMS instead of ERM | 2-layer MLP · Adult | accuracy: 83.38 -> 83.33 | No clear effect |
| use FAMS instead of ERM | ResNet-18 · CelebA | group sufficiency gap: 7.13 -> 5.29 | Helped |
| use FAMS instead of ERM | ResNet-18 · CelebA | accuracy: 79.67 -> 79.87 | No clear effect |
| set lambda near 0 (no prior regularization in lower level) (ablation) | FAMS · Amazon review | accuracy and group sufficiency gap | Hurt |
| set very large lambda (50) (ablation) | FAMS · Amazon review | accuracy: 55% | Hurt |
| lambda in [0.2,1] (ablation) | FAMS · Amazon review | accuracy and group sufficiency gap | Helped |
Closest papers by meaning
- Fair Dataset Distillation via Cross-Group Barycenter Alignment (ICML 2026 · similarity 0.90)
- On Group Sufficiency Under Label Bias (NeurIPS 2025 · similarity 0.90)
- Popularizing Fairness: Group Fairness and Individual Welfare (AAAI 2023 · similarity 0.90)
- Subgroups Matter for Robust Bias Mitigation (ICML 2025 · similarity 0.89)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.88)
- Enhancing Group Fairness in Online Settings Using Oblique Decision Forests (ICLR 2024 · similarity 0.88)
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