Learning with privileged and sensitive information: a gradient-boosting approach

We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learner...

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Detalles Bibliográficos
Autores principales: Yan, Siwen, Odom, Phillip, Pasunuri, Rahul, Kersting, Kristian, Natarajan, Sriraam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679679/
https://www.ncbi.nlm.nih.gov/pubmed/38028664
http://dx.doi.org/10.3389/frai.2023.1260583

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10679679/
https://www.ncbi.nlm.nih.gov/pubmed/38028664
http://dx.doi.org/10.3389/frai.2023.1260583

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