  We study the question of learning an adversarially robust predictor. We show
that any hypothesis class $\mathcal{H}$ with finite VC dimension is robustly
PAC learnable with an improper learning rule. The requirement of being improper
is necessary as we exhibit examples of hypothesis classes $\mathcal{H}$ with
finite VC dimension that are not robustly PAC learnable with any proper
learning rule.
