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New statistical framework analyzes overfitting in high-dimensional regression

By PulseAugur Editorial · [1 sources] ·

This paper delves into the statistical behavior of high-dimensional ridgeless least-squares estimators, examining how prediction risk is affected by sample size and feature dimension growth. It introduces a generalized spiked population covariance model to analyze the influence of latent factors and their eigenvalues on overfitting. The research highlights that the alignment between regression coefficients and the covariance matrix's eigenspaces is crucial for determining whether interpolation leads to benign, tempered, or catastrophic overfitting, establishing sharp prediction risk limits under minimal moment conditions. AI

IMPACT Provides a theoretical framework for understanding generalization in overparameterized models, relevant for developing more robust AI systems.

RANK_REASON Academic paper published on arXiv detailing statistical theory. [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 →

New statistical framework analyzes overfitting in high-dimensional regression

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Academic paper published on arXiv detailing statistical theory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zhijun Liu, Dandan Jiang ·

    High-dimensional ridgeless least squares interpolation under spiked covariance structures

    arXiv:2608.07281v1 Announce Type: cross Abstract: This paper investigates the asymptotic behavior of the out-of-sample prediction risk of the high-dimensional ridgeless least-squares estimator when the feature dimension $p$ and the sample size $n$ grow proportionally. We consider…

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