We discuss new techniques for approximating the mean of a Gaussian in the presence of a large fraction of adversarial errors. We show that by taking advantage of higher moments of these distributions, we can obtain errors close to the information-theoretic optimum, and present an application of this to learning mixtures of spherical Gaussians.

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