Robust XGBoosting for Regression
Researchers analyze the robustness of XGBoost against vertical outliers and leverage points in regression tasks.
The study examines how standard XGBoost implementations perform under non-ideal data conditions. While quadratic loss is standard, the authors investigate the impact of outliers on model stability and performance, providing insights for more robust gradient boosting applications.