A novel gradient boosting framework is proposed where shallow neural networks are employed as "weak learners". General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank.

A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets.

An ablation study is performed to shed light on the effect of each model components and model hyperparameters.