AI RESEARCH
Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
arXiv CS.LG
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ArXi:2604.03146v1 Announce Type: cross We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to non-Gaussian settings, we derive an asymptotic min-max characterization of key statistics, enabling approximation of the mean $\mu_{\hat{\theta}}$ and covariance $C_{\hat{\theta}}$ of the ERM estimator $\hat{\theta