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.
- arXiv
- benign overfitting
- interpolation limit
- kernel learning curve
- KRR
- large-dimensional theory
- learning curves
- spectral algorithms
- under-regularized regime
- regularization path
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