Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates grows proportionally to the number of samples. We provide a tight characterization of the test error, measured in mean squared error, in terms only of the population quantities of the true data, as well as first and second order statistics of the augmentation scheme. Our results are valid under misspecified feature maps, and for any network architecture where only the last readout layer is trained, and the rest of the network is either frozen or randomly initialized. We specify our results in the case of Gaussian ...
| Authors | Lucas Morisset, Alain Durmus, Adrien Hardy |
|---|---|
| Primary category | stat.ML |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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