Research map / Learning theory and algorithms / Fairness
Fair and Optimal Classification via Post-Processing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Post-process a score function via Wasserstein-barycenter optimal transport (instead of FairProjection) | logistic regression scores · ACSIncome and BiasBios | accuracy vs Delta_DP tradeoff | Helped |
| Post-processing with the proposed method versus FairProjection, with advantage largest in multi-class settings | logistic regression scores · BiasBios (multi-class) | tradeoff curves | Helped |
| Increase training data (sample size) for post-processing | proposed post-processing · ACSIncome multi-group multi-class | Delta_DP gap to 0 | No clear effect |
| Calibrate scores before post-processing | proposed post-processing · general | accuracy | No clear effect |
Closest papers by meaning
- Demystifying the Optimal Fair Classifier in Multi-Class Classification (ICML 2026 · similarity 0.91)
- Post-hoc bias scoring is optimal for fair classification (ICLR 2024 · similarity 0.91)
- Demographic parity in regression and classification within the unawareness framework (NeurIPS 2026 · similarity 0.90)
- FairWASP: Fast and Optimal Fair Wasserstein Pre-processing (AAAI 2024 · similarity 0.90)
- A Sequentially Fair Mechanism for Multiple Sensitive Attributes (AAAI 2024 · similarity 0.89)
- Efficient Fairness-Performance Pareto Front Computation (NeurIPS 2025 · similarity 0.88)
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