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Distributionally robust learning-to-rank under the Wasserstein metric

Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional data shift, and malicious adversaries who wish...

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Detalles Bibliográficos
Autores principales: Sotudian, Shahabeddin, Chen, Ruidi, Paschalidis, Ioannis Ch.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062629/
https://www.ncbi.nlm.nih.gov/pubmed/36996130
http://dx.doi.org/10.1371/journal.pone.0283574