Research map / Learning theory and algorithms / Clustering
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Include the hidden binary attribute and fairness-related bias term in sparse regression | Fair sparse regression · Synthetic data | exact hidden-attribute recovery: 100% | Helped |
| Add the debiasing/fairness constraint to the regression model | Fair sparse regression · Theoretical analysis | regression performance | No clear effect |
Closest papers by meaning
- Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation (ICML 2022 · similarity 0.89)
- Structured Sparse Regression via Greedy Hard Thresholding (NeurIPS 2016 · similarity 0.87)
- Robust Sparse Regression with Non-Isotropic Designs (NeurIPS 2024 · similarity 0.86)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.86)
- Fair Submodular Cover (ICLR 2025 · similarity 0.85)
- Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings (NeurIPS 2026 · similarity 0.85)
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