Before revision: We study the problem of robustly estimating the mean of a d-dimensional distribution given N examples, where \varepsilon N examples may be arbitrarily corrupted and most coordinates of every example may be missing  .
After revision: We study the problem of robustly estimating the mean of a d-dimensional distribution given N examples, where \varepsilon N examples may be arbitrarily corrupted and most coordinates of every example may be missing and \varepsilon N examples may be arbitrarily corrupted .