New analysis method for heavy-tailed data in machine learning

Researchers have developed a new method to analyze the performance of empirical risk minimization (ERM) in machine learning when dealing with datasets that have heavy-tailed distributions. This approach introduces a functional order parameter to describe the random effective problem associated with each coefficient. Using the replica method, the study precisely characterizes the generalization error in a high-dimensional limit and establishes a heavy-tail universality law, detailing how typical errors relate to prediction reliability and the Bayes-optimal prediction error. AI

IMPACT Provides a theoretical framework for understanding and improving machine learning model performance on datasets with extreme values.

RANK_REASON Academic paper detailing a new theoretical analysis method for machine learning on heavy-tailed data. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• empirical risk minimization
• linear regression

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