  We consider a robust estimation of linear regression coefficients. In this
note, we focus on the case where the covariates are sampled from an
$L$-subGaussian distribution with unknown covariance, the noises are sampled
from a distribution with a bounded absolute moment and both covariates and
noises may be contaminated by an adversary. We derive an estimation error
bound, which depends on the stable rank and the condition number of the
covariance matrix of covariates with a polynomial computational complexity of
estimation.
