Batch Active Learning via Coordinated Matching

Understand

Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort.

• In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in parallel.
• This motivates us to study batch active learning, which iteratively selects batches of $k>1$ examples to be labeled.
• We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by attempting to approximate their behavior when applied for $k$ steps.