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New theory characterizes Gaussian universality breakdown in high-dimensional ERM

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new theoretical framework to understand how statistical learning models behave with non-Gaussian data. The study extends the Convex Gaussian Min-Max Theorem to analyze high-dimensional empirical risk minimization, providing an asymptotic characterization of key statistics like the estimator's mean and covariance. This work clarifies the boundaries of Gaussian universality in empirical risk minimization and offers insights into the asymptotic equivalence of regularizers. AI

RANK_REASON This is a theoretical paper published on arXiv detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko, Zhenyu Liao ·

    Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization

    arXiv:2604.03146v2 Announce Type: replace Abstract: 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 …

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