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 u...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Society for Industrial and Applied Mathematics
2018
|
| Online Access: | http://hdl.handle.net/1721.1/116214 https://orcid.org/0000-0003-0048-2559 https://orcid.org/0000-0002-9937-0049 https://orcid.org/0000-0001-7047-0495 |