<ul><li>Benign overfitting refers to the phenomenon where an over-parameterized model fits the training data perfectly, including noise in the data, but still generalizes well to the unseen test data.</li><li>This study focuses on understanding benign overfitting in the Out-of-Distribution (OOD) regime for over-parameterized linear models under covariate shift.</li><li>The authors provide non-asymptotic guarantees that benign overfitting occurs in standard ridge regression, even in the OOD regime under certain structural conditions of the target covariance.</li><li>Theoretical results show that Principal Component Regression (PCR) achieves a faster rate of O(1/n) for the excess risk compared to standard ridge regression's slower rate of O(1/√n) for a more general family of target covariance matrices.</li></ul>

Benign Overfitting in Out-of-Distribution Generalization of Linear Models

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