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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2023
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| 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 |