Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression
We compare the instance-wise, finite-sample risks of monotone spectral filters for linear regression, a broad class of estimators including principal component regression (PCR), gradient descent (GD), and ridge regression.
By Juno Kim, Hengyu Fu, Peter Bartlett, Jason D. Lee, Jingfeng WuarXiv - stat.ML
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Originally published by arXiv - stat.ML on Oct 1, 2026.
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