Before revision:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging  malicious noise model, where an adversary may corrupt both the unlabeled data distribution and the labels.
After revision:  Though recent works have established attribute-efficient learning algorithms under various types of label noise (e.g. bounded noise), it remains an open question of when and how s-sparse halfspaces can be efficiently learned under the challenging  malicious noise model, where an adversary may corrupt both the unlabeled examples and the labels.