We obtain robust and computationally efficient estimators for learning several linear models that achieve statistically optimal convergence rate under minimal distributional assumptions. Concretely, we assume our data is drawn from a $k$-hypercontractive distribution and an $ε$-fraction is adversarially corrupted. We then describe an estimator that converges to the optimal least-squares minimiz…