Researchers have extended the principles of Gaussian universality and the convex Gaussian min-max theorem (CGMT) to dependent data settings. This work demonstrates that Gaussian universality remains applicable to high-dimensional logistic regression even with block dependence, m-dependence, and certain mixing conditions. Additionally, a new CGMT framework has been developed to account for correlations in both covariates and observations. These advancements allow for a better understanding of the impact of data augmentation practices on asymptotic risk in deep learning. AI
IMPACT Provides a theoretical foundation for understanding data augmentation in deep learning under dependent data conditions.
RANK_REASON The cluster contains an academic paper detailing theoretical advancements in statistics. [lever_c_demoted from research: ic=1 ai=0.7]
- convex Gaussian min-max theorem (CGMT)
- data augmentation
- deep learning
- Gaussian universality
- high-dimensional logistic regression
- Matthew Esmaili Mallory
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