3 papers
math.ST2026
OT-FairBoost: Optimal Transport-Guided Gradient Boosting for Fairness Regularization on Tabular Data
Veronika Shilova, Abdoulaye Sakho, Younes Boumoussou +3
Although neural-based machine learning models have received a lot of attention recently, tree-based models such as gradient boosting are competitive for tabular data and therefore…
cs.LG2025
Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier +1
This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic sa…
stat.ML2024
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In thi…