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dc.contributor.author | Fabra-Boluda, Raúl | es_ES |
dc.contributor.author | Ferri, C. | es_ES |
dc.contributor.author | Ramírez Quintana, María José | es_ES |
dc.contributor.author | Martínez-Plumed, Fernando | es_ES |
dc.date.accessioned | 2024-10-01T18:05:40Z | |
dc.date.available | 2024-10-01T18:05:40Z | |
dc.date.issued | 2024-09-01 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/209085 | |
dc.description.abstract | [EN] The evaluation of machine learning systems has typically been limited to performance measures on clean and curated datasets, which may not accurately reflect their robustness in real-world situations where data distribution can vary from learning to deployment, and where truthfully predict some instances could be more difficult than others. Therefore, a key aspect in understanding robustness is instance difficulty, which refers to the level of unexpectedness of system failure on a specific instance. We present a framework that evaluates the robustness of different ML models using item response theory-based estimates of instance difficulty for supervised tasks. This framework evaluates performance deviations by applying perturbation methods that simulate noise and variability in deployment conditions. Our findings result in the development of a comprehensive taxonomy of ML techniques, based on both the robustness of the models and the difficulty of the instances, providing a deeper understanding of the strengths and limitations of specific families of ML models. This study is a significant step towards exposing vulnerabilities of particular families of ML models. | es_ES |
dc.description.sponsorship | This work was funded by the Norwegian Research Council Grant 329745 Machine Teaching for Explainable AI, the MIT-Spain-INDITEX Sustainability Seed Fund under Project COST-OMIZE, CIPROM/2022/6 (FASSLOW) funded by Generalitat Valenciana, the EC H2020-EU Grant Agreement No. 952215 (TAILOR), and Spanish Grant PID2021-122830OB-C42 (SFERA) funded by MCIN/AEI/10.13039/501100011033 and 'ERDF A way of making Europe'. RFB is supported by predoctoral Grant PRE2019-090892. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IOP Publishing | es_ES |
dc.relation.ispartof | Machine Learning: Science and Technology | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Robustness | es_ES |
dc.subject | Noise | es_ES |
dc.subject | Instance difficulty | es_ES |
dc.subject | Supervised learning | es_ES |
dc.subject | Item response theory | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Unveiling the robustness of machine learning families | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1088/2632-2153/ad62ab | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122830OB-C42/ES/METODOS FORMALES ESCALABLES PARA APLICACIONES REALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/952215/EU/Integrating Reasoning, Learning and Optimization/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/RCN//329745/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//CIPROM%2F2022%2F6//Tecnologías de Aprendizaje y Razonamiento Rápido y Lento/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//PRE2019-090892/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Fabra-Boluda, R.; Ferri, C.; Ramírez Quintana, MJ.; Martínez-Plumed, F. (2024). Unveiling the robustness of machine learning families. Machine Learning: Science and Technology. 5(3). https://doi.org/10.1088/2632-2153/ad62ab | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1088/2632-2153/ad62ab | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 5 | es_ES |
dc.description.issue | 3 | es_ES |
dc.identifier.eissn | 2632-2153 | es_ES |
dc.relation.pasarela | S\525635 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Research Council of Norway | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |