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...

Descripción completa

Detalles Bibliográficos
Autores: Emami, Seyedsaman, Martínez Muñoz, Gonzalo, Hernández Lobato, Daniel
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
id ES_dd6f2e5cfe6b31ca0db3e8f1ca376e7a
oai_identifier_str oai:repositorio.uam.es:10486/743720
network_acronym_str ES
network_name_str España
repository_id_str
spelling Robust-multi-task gradient boostingEmami, SeyedsamanMartínez Muñoz, GonzaloHernández Lobato, DanielRobust-multi-task learningGradient boostingOutlier detectionInformáticaThe 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 environmentsThe authors acknowledge financial support from the project PID2022-139856NB-I00, funded by MCIN/AEI/10.13039/501100011033/FEDER, UE; from project IDEA-CM (TEC-2024/COM-89), funded by the Autonomous Community of Madrid; and from the ELLIS Unit MadridElsevierDepartamento de Ingeniería InformáticaEscuela Politécnica SuperiorGobierno de EspañaComunidad de Madrid20252025-12-07research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10486/743720https://dx.doi.org/10.1016/j.eswa.2025.130696reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7437202026-06-23T12:46:27Z
dc.title.none.fl_str_mv Robust-multi-task gradient boosting
title Robust-multi-task gradient boosting
spellingShingle Robust-multi-task gradient boosting
Emami, Seyedsaman
Robust-multi-task learning
Gradient boosting
Outlier detection
Informática
title_short Robust-multi-task gradient boosting
title_full Robust-multi-task gradient boosting
title_fullStr Robust-multi-task gradient boosting
title_full_unstemmed Robust-multi-task gradient boosting
title_sort Robust-multi-task gradient boosting
dc.creator.none.fl_str_mv Emami, Seyedsaman
Martínez Muñoz, Gonzalo
Hernández Lobato, Daniel
author Emami, Seyedsaman
author_facet Emami, Seyedsaman
Martínez Muñoz, Gonzalo
Hernández Lobato, Daniel
author_role author
author2 Martínez Muñoz, Gonzalo
Hernández Lobato, Daniel
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
Gobierno de España
Comunidad de Madrid
dc.subject.none.fl_str_mv Robust-multi-task learning
Gradient boosting
Outlier detection
Informática
topic Robust-multi-task learning
Gradient boosting
Outlier detection
Informática
description 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
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-07
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10486/743720
https://dx.doi.org/10.1016/j.eswa.2025.130696
url https://hdl.handle.net/10486/743720
https://dx.doi.org/10.1016/j.eswa.2025.130696
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421859597975552
score 15,81155

Similares en LA Referencia