Preprint
Jul 2026
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
OT-FairBoost is introduced, a novel in-processing framework that incorporates a Wasserstein-2 distance penalty directly into the objective function of gradient-boosted trees to efficiently optimize group fairness criteria such as Demographic Parity and Equalized Odds on neural-based predictions.
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou et al.
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