Cargando…
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...
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: | |
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 |
Ejemplares similares
-
Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting
por: Bakasa, Wilson, et al.
Publicado: (2023) -
Predicting energy use in construction using Extreme Gradient Boosting
por: Han, Jiaming, et al.
Publicado: (2023) -
AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong
por: Blüml, Jannis, et al.
Publicado: (2023) -
Declarative Learning-Based Programming as an Interface to AI Systems
por: Kordjamshidi, Parisa, et al.
Publicado: (2022) -
Active feature elicitation: An unified framework
por: Das, Srijita, et al.
Publicado: (2023)