Sharp Guarantees and Optimal Performance for Inference in Binary and Gaussian-Mixture Models †

We study convex empirical risk minimization for high-dimensional inference in binary linear classification under both discriminative binary linear models, as well as generative Gaussian-mixture models. Our first result sharply predicts the statistical performance of such estimators in the proportion...

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Detalles Bibliográficos
Autores principales: Taheri, Hossein, Pedarsani, Ramtin, Thrampoulidis, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910944/
https://www.ncbi.nlm.nih.gov/pubmed/33573327
http://dx.doi.org/10.3390/e23020178

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910944/
https://www.ncbi.nlm.nih.gov/pubmed/33573327
http://dx.doi.org/10.3390/e23020178

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