Before revision: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
After revision: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu .