Before revision: This article studies basis pursuit, i.e. minimum \ell_1-norm interpolation, in sparse linear regression with additiveerrors. No conditions on the errors are imposed.
After revision: This article develops a general theory for minimum-norm interpolated estimators in linear models in the presence of additive, potentially adversarial, errors. In particular, no conditions on the errors are imposed.