In high-dimensional convex ERM with non-Gaussian data, the projection of the estimator onto a test covariate asymptotically follows the convolution of a generally non-Gaussian term with an independent centered Gaussian whose variance is the trace of the estimator covariance times the data second-mom
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Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
In high-dimensional convex ERM with non-Gaussian data, the projection of the estimator onto a test covariate asymptotically follows the convolution of a generally non-Gaussian term with an independent centered Gaussian whose variance is the trace of the estimator covariance times the data second-mom