  We present a new active learning algorithm that adaptively partitions the
input space into a finite number of regions, and subsequently seeks a distinct
predictor for each region, both phases actively requesting labels. We prove
theoretical guarantees for both the generalization error and the label
complexity of our algorithm, and analyze the number of regions defined by the
algorithm under some mild assumptions. We also report the results of an
extensive suite of experiments on several real-world datasets demonstrating
substantial empirical benefits over existing single-region and non-adaptive
region-based active learning baselines.
