  We consider the problem of learning linear classifiers when both features and
labels are binary. In addition, the features are noisy, i.e., they could be
flipped with an unknown probability. In Sy-De attribute noise model, where all
features could be noisy together with same probability, we show that $0$-$1$
loss ($l_{0-1}$) need not be robust but a popular surrogate, squared loss
($l_{sq}$) is. In Asy-In attribute noise model, we prove that $l_{0-1}$ is
robust for any distribution over 2 dimensional feature space. However, due to
computational intractability of $l_{0-1}$, we resort to $l_{sq}$ and observe
that it need not be Asy-In noise robust. Our empirical results support Sy-De
robustness of squared loss for low to moderate noise rates.
