Title: Adversarial Boot Camp: label free certified robustness in one epoch. Abstract: Machine learning models are vulnerable to adversarial attacks.  One approach to addressing this vulnerability is certification, which focuses on models that are guaranteed to be robust for a given perturbation size.  A drawback of recent certified models is that they are stochastic: they require multiple computationally expensive model evaluations with random noise added to a given image. In our work, we present a deterministic certification approach which results in a certifiably robust model. This approach is based on an equivalence between training with a particular regularized loss, and the expected values of Gaussian averages. We achieve certified models on ImageNet-1k by retraining a model with this loss for one epoch without the use of label information.