List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians

STOC '18: Symposium on Theory of Computing Los Angeles CA USA June, 2018, pp. 1047-1060, 2018.

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Abstract:

We study the problem of list-decodable (robust) Gaussian mean estimation and the related problem of learning mixtures of separated spherical Gaussians. In the former problem, we are given a set T of points in n with the promise that an α-fraction of points in T, where 0< α < 1/2, are drawn from an unknown mean identity covariance Gaussian...More

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