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 t...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/743720 |
| Acceso en línea: | https://hdl.handle.net/10486/743720 https://dx.doi.org/10.1016/j.eswa.2025.130696 |
| Access Level: | acceso abierto |
| Palabra clave: | Robust-multi-task learning Gradient boosting Outlier detection Informática |
| Sumario: | 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 |
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