A Regularization-Sharpness Tradeoff for Linear Interpolators
This new framework could explain why some overparameterized models generalize so well.
Researchers have proposed a new 'regularization-sharpness tradeoff' to explain model performance in overparameterized settings, where classical bias-variance theory breaks down. The work extends the 'interpolating information criterion' to ℓ^p regularizers (p≥2) and the LASSO, decomposing selection penalties into alignment and geometric sharpness terms. Empirical validation on real-world datasets with random Fourier features shows the framework can distinguish high-performing linear interpolators from weaker ones, offering a fresh lens for model selection.
Why It Matters
It provides a crucial new theoretical tool for understanding and selecting performant models in the modern overparameterized regime.