Two faces of active learning

An active learner has a collection of data points, each with a label that is initially hidden but can be obtained at some cost. Without spending too much, it wishes to find a classifier that will accurately map points to labels. There are two common intuitions about how this learning process should be organized: (i) by choosing query points that shrink the space of candidate classifiers as rapidly as possible; and (ii) by exploiting natural clusters in the (unlabeled) data set. Recent research has yielded learning algorithms for both paradigms that are efficient, work with generic hypothesis classes, and have rigorously characterized labeling requirements. Here we survey these advances by focusing on two representative algorithms and discussing their mathematical properties and empirical performance.