arXiv Statistics ML By Juno Kim, Hengyu Fu, Peter Bartlett, Jason D. Lee, Jingfeng Wu

Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression

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arXiv Machine Learning
Jun 10

Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization

arXiv:2509. 17251v2 Announce Type: replace-cross Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems.

By Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu
arXiv Machine Learning
Jul 9

Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer

arXiv:2501. 10870v2 Announce Type: replace-cross Abstract: The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rates for fixed-bandwidth Gaussian kernel spectral algorithms when the true regression function resides in a Sobolev space, and (2) to apply Gaussian spectral algorithms for achieving robust and adaptive transfer learning under concept shift.

By Haotian Lin, Matthew Reimherr