Robust Learning of Mixtures of Gaussians

We resolve one of the major outstanding problems in robust statistics. In particular, if X is an evenly weighted mixture of two arbitrary d-dimensional Gaussians, we devise a polynomial time algorithm that given access to samples from X an ∊-fraction of which have been adversarially corrupted, learns X to error poly(∊) in total variation distance.