Derives distribution-dependent generalization bounds for tuning L1/L2 regularization in multi-task linear regression that do not degrade with feature dimension d under sub-Gaussian i.i.d. assumptions and are sharper than prior uniform bounds for large d.
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Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
Derives distribution-dependent generalization bounds for tuning L1/L2 regularization in multi-task linear regression that do not degrade with feature dimension d under sub-Gaussian i.i.d. assumptions and are sharper than prior uniform bounds for large d.