  Generating labeled training datasets has become a major bottleneck in Machine
Learning (ML) pipelines. Active ML aims to address this issue by designing
learning algorithms that automatically and adaptively select the most
informative examples for labeling so that human time is not wasted labeling
irrelevant, redundant, or trivial examples. This paper proposes a new approach
to active ML with nonparametric or overparameterized models such as kernel
methods and neural networks. In the context of binary classification, the new
approach is shown to possess a variety of desirable properties that allow
active learning algorithms to automatically and efficiently identify decision
boundaries and data clusters.
