Active learning is defined by contrast to the passive model of supervised learning where all the labels for learning are obtained without reference to the learning algorithm, while in active learning the learner interactively chooses which data points to label. The hope of active learning is that interaction can substantially reduce the number of labels required, making solving problems via machine learning more practical. This hope is known to be valid in certain special cases, both empirically and theoretically.

Variants of active learning have been investigated over several decades and fields. The focus of this tutorial is on general techniques which are applicable to many problems. At a mathematical level, this corresponds to approaches with provable guarantees under weakest-possible assumptions since real problems are more likely to fit algorithms which work under weak assumptions.

We believe this tutorial should be of broad interest. People working on or using supervised learning are often confronted with the need for more labels, where active learning can help. Similarly, in reinforcement learning, generalizing while interacting in more complex ways is an active research topic. Please join us.