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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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| Publicado no: | Entropy (Basel) |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7910944/ https://ncbi.nlm.nih.gov/pubmed/33573327 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e23020178 |
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