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.
Generalized cross-validation as a method for choosing a good ridge parameter
2 Pith papers cite this work. Polarity classification is still indexing.
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A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.
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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.
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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.