Research map / Learning theory and algorithms / Fairness
Fairness with Adaptive Weights
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adaptive sample reweighing (vs. equal group reweighing, undersampling, oversampling) | logistic regression · COMPAS | disparate TPR: 0.24 vs 1.14 for ASR, 5.77 for undersampling | Helped |
| adaptive sample reweighing | logistic regression · Adult | accuracy: 82.44 vs 78.36 (reweighing) and 79.48 (undersampling) | Helped |
| adaptive sample reweighing | logistic regression · Adult | disparate impact: 17.14 vs 0.31 for ASR | Hurt |
| adaptive sample reweighing | RLS regression · Law School / CRIME | statistical parity: Law School SP 2.16% vs FWB 2.13%; CRIME SP 11.47% vs FWB 12.10% | Mixed |
Closest papers by meaning
- Achieving Fairness at No Utility Cost via Data Reweighing with Influence (ICML 2022 · similarity 0.89)
- Self-Supervised Fair Representation Learning without Demographics (NeurIPS 2022 · similarity 0.89)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.87)
- Fairness without Demographics through Shared Latent Space-Based Debiasing (AAAI 2024 · similarity 0.86)
- Mitigating Label Bias in Machine Learning: Fairness through Confident Learning (AAAI 2024 · similarity 0.86)
- Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness (NeurIPS 2025 · similarity 0.86)
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