Robust Learning of Mixtures of Gaussians

University of California, San Diego（加利福尼亚大学圣地亚哥分校）

Daniel M. Kane

英文摘要：

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 $\eps$-fraction of which have been adversarially corrupted, learns $X$ to error $\poly(\eps)$ in total variation distance.

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