This paper presents an algorithm for estimating the mean of a heavy-tailed random variable from an adversarially corrupted sample of N independent observations.
We construct an algorithm for estimating the mean of a heavy-tailed random variable when given an adversarial corrupted sample of N independent observations. The only assumption we make on the distribution of the noncorrupted (or informative) data is the existence of a covariance matrix Σ, unknown to the statistician.