Robust Estimators in High Dimensions without the Computational Intractability

Ilias Diakonikolas, Gautam Kamath, Daniel Kane +3

We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an $\epsilon$-fraction of the samples. Such questions…