Theoretical Foundations of Active Learning

Steve Hanneke

Thesis Committee: Avrim Blum, Sanjoy Dasgupta, Larry Wasserman, Eric P. Xing


I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of convergence achievable by active learning, under arious noise models and under general conditions on the hypothesis class. I also study the theoretical advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms with asymptotically superior label complexity. Finally, I study generalizations of active learning to more general forms of interactive statistical learning.