Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Authors: Simon Segert
Presentation: ICLR 2024,Poster

Abstract: We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect to a matrix norm of Schatten type.  We derive simple and explicit formulas for the optimal estimator in the cases of Nuclear and  Spectral norms (with the Frobenius case recovering ridge regression). Additionally, we analytically derive the generalization error in multiple random matrix ensembles, and compare with Ridge regression. Finally, we conduct an extensive simulation study, in which we show that the cross-validated Nuclear and Spectral regressors can outperform Ridge in several circumstances.