Robust-multi-task gradient boosting

The objective of this study is to develop a robust boosting framework capable of handling heterogeneous and outlier tasks in Multi-Task Learning (MTL). Conventional MTL methods assume strong relatedness among tasks, which often fails in real-world scenarios involving adversarial or unaligned tasks that degrade performance. To address this limitation, we propose Robust Multi-Task Gradient Boosting (R-MTGB), a novel ensemble framework that explicitly models task heterogeneity within the gradient boosting paradigm. The methodology structures learning into three sequential stages: (1) shared representation learning to extract common patterns across tasks, (2) outlier-aware partitioning using a learnable task-specific parameter to separate and reweight outlier and non-outlier tasks, and (3) task-specific fine-tuning to refine individual predictors. Extensive experiments on both synthetic and real-world datasets demonstrate that R-MTGB consistently improves predictive accuracy, effectively identifies outlier tasks, and enhances generalization compared to state-of-the-art methods. The achieved results confirm that R-MTGB not only ensures robust performance and interpretability through task-level outlier scores but also provides a scalable and principled framework for reliable multi-task learning in heterogeneous environments.