Web: http://arxiv.org/abs/2206.08092

June 17, 2022, 1:10 a.m. | Hongjie Chen, Tommaso d'Orsi

cs.LG updates on arXiv.org arxiv.org

We consider the robust linear regression model $\boldsymbol{y} = X\beta^* +
\boldsymbol{\eta}$, where an adversary oblivious to the design $X \in
\mathbb{R}^{n \times d}$ may choose $\boldsymbol{\eta}$ to corrupt all but a
(possibly vanishing) fraction of the observations $\boldsymbol{y}$ in an
arbitrary way. Recent work [dLN+21, dNS21] has introduced efficient algorithms
for consistent recovery of the parameter vector. These algorithms crucially
rely on the design matrix being well-spread (a matrix is well-spread if its
column span is far from any …

arxiv lg linear linear regression on regression

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