  We develop a new active learning algorithm for the streaming setting
satisfying three important properties: 1) It provably works for any classifier
representation and classification problem including those with severe noise. 2)
It is efficiently implementable with an ERM oracle. 3) It is more aggressive
than all previous approaches satisfying 1 and 2. To do this we create an
algorithm based on a newly defined optimization problem and analyze it. We also
conduct the first experimental analysis of all efficient agnostic active
learning algorithms, evaluating their strengths and weaknesses in different
settings.
