Research map / Learning theory and algorithms / Fairness
A Fair Classifier Using Kernel Density Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use KDE-based direct DDP measure instead of covariance proxy (Zafar et al.) and GAN-based fair classifier | KDE fair classifier · Synthetic Moon dataset | accuracy-DDP tradeoff | Helped |
| use KDE-based regularizer instead of adversarial learning (Zhang et al.) | KDE fair classifier · Synthetic Moon dataset | training stability across seeds | Helped |
| use KDE-based fairness regularizer vs baselines | KDE fair classifier · COMPAS | accuracy-fairness tradeoff (DDP/DEO) | Helped |
| use KDE-based fairness regularizer vs Agarwal et al. | KDE fair classifier · Adult Census | accuracy-fairness tradeoff | No clear effect |
| vary KDE bandwidth h from 0.1 to 0.001 (ablation) | KDE fair classifier · Gaussian N(0.5,1) pmf estimate | estimated Pr(Y~=1): 0.5055 -> 0.5091 | No clear effect |
| extend KDE fairness to multiclass via one-vs-rest probability estimate | KDE fair classifier · 3-way Gaussian synthetic | accuracy-DDP tradeoff | Helped |
Closest papers by meaning
- Demystifying the Optimal Fair Classifier in Multi-Class Classification (ICML 2026 · similarity 0.90)
- Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning (NeurIPS 2025 · similarity 0.89)
- FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods (ICLR 2024 · similarity 0.88)
- Fairness without Demographics through Shared Latent Space-Based Debiasing (AAAI 2024 · similarity 0.88)
- The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning (NeurIPS 2024 · similarity 0.88)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.88)
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