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Variants of the Lasso orℓ1-penalized regression have been proposed to accommodate for presence of measurement errors in the covariates. Theoretical guarantees of these estimates have been established for some oracle values of the regularization parameters which are not known in practice. Data-driven tuning such as cross-validation has not been studied when covariates contain measurement errors. We demonstrate that in the presence of error-in-covariates, even when using a Lasso-variant thatdoi:10.6084/m9.figshare.9883073 fatcat:rglz673chnek5fangosrueibla