Robustly Learning a Gaussian: Getting Optimal Error, Efficiently

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently

We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise | where an "-fraction of our samples were chosen by an adversary. We give robust estimators that achieve estimation error O(ϵ) in the total variation distance, which is optimal up...

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Bibliographic Details
Main Authors: Stewart, Alistair (Author), Diakonikolas, Ilias (Contributor), Kamath, Gautam Chetan (Contributor), Kane, Daniel M (Contributor), Li, Jerry Zheng (Contributor), Moitra, Ankur (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mathematics (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor)
Format: Article
Language:English
Published: Society for Industrial and Applied Mathematics, 2018-06-11T17:27:24Z.
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