Research map / Learning theory and algorithms / Fairness
Equal Opportunity of Coverage in Fair Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply BFQR instead of CQR | Quantile Regression · Synthetic data | Mean prediction interval width: 16.02±0.04 -> 15.96±0.13 | Helped |
| Apply BFQR instead of CFQR | Quantile Regression · Synthetic data | Mean Max Coverage Gap: 0.19±0.09 -> 3.82±0.45 | Hurt |
| Use continuous joint intervals instead of disjoint intervals (ablation) | BFQR · Adult | Mean Max Coverage Gap: 3.05±0.35 -> 2.90±0.37 | Helped |
| Use continuous joint intervals instead of disjoint intervals (ablation) | BFQR · Synthetic data | Mean prediction interval width: 15.96±0.13 -> 16.24±0.21 | Hurt |
| Use 10 bins rather than other evaluated bin counts (ablation) | BFQR and BFQR* · Synthetic data | EOC | Helped |
| Use robust approximate subgradient optimization rather than BFGS-SQP | BFQR optimization · Synthetic data optimization | Running Time (seconds): 78.11±41.98 -> 33.62±14.78 | Helped |
Closest papers by meaning
- Achievable Fairness on Your Data With Utility Guarantees (NeurIPS 2024 · similarity 0.88)
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- Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings (NeurIPS 2026 · similarity 0.87)
- Counterfactual Fairness by Combining Factual and Counterfactual Predictions (NeurIPS 2024 · similarity 0.87)
- The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning (NeurIPS 2024 · similarity 0.87)
- Conformal Prediction Sets Can Cause Disparate Impact (ICLR 2025 · similarity 0.87)
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