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Spectral algorithms in large dimensions reveal three learning curve regimes

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv explores the learning curves and benign overfitting phenomena in spectral algorithms within large-dimensional settings. The study characterizes the excess risk across different regularization paths, identifying three distinct regimes: over-regularized, under-regularized, and interpolation. Benign overfitting is shown to occur in the latter two regimes under specific conditions related to the smoothness of the regression function. AI

IMPACT Provides theoretical insights into the behavior of spectral algorithms, potentially informing future model development and analysis.

RANK_REASON Academic paper published on arXiv detailing theoretical findings in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Spectral algorithms in large dimensions reveal three learning curve regimes

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Weihao Lu, Qian Lin, Yingcun Xia, Dongming Huang ·

    Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions

    arXiv:2604.23212v1 Announce Type: new Abstract: Existing large-dimensional theory for spectral algorithms resolves either the optimally tuned point or the interpolation limit, but leaves the under-regularized regime unexplored. We study the learning curve and benign overfitting o…

  2. arXiv stat.ML TIER_1 English(EN) · Dongming Huang ·

    Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions

    Existing large-dimensional theory for spectral algorithms resolves either the optimally tuned point or the interpolation limit, but leaves the under-regularized regime unexplored. We study the learning curve and benign overfitting of spectral algorithms in the large-dimensional s…

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