Reviews: Machine Teaching of Active Sequential Learners

This paper considers the problem of teaching an active sequential *machine learner* (e.g., an active learning algorithm), with a teacher which can "fake" the labels/outcomes of training examples with the goal of steering the learner faster to the goal state. The authors refer to such teacher a "planning teacher", as opposed to a "naive teacher" which is often considered in the classical machine teaching problems. This setting differs from conventional machine teaching settings, in that In classical machine teaching setting, the teacher can only choose among a given set of training examples that are consistent with the target concept, and is often not allowed to provide inconsistent examples. The majority of the existing work in machine teaching considers teaching a "passive learner", with a few exceptions (see additional reference in the comments below). The assumption that the teacher can choose the data-generation distribution makes it a very powerful teacher with a much richer action set than conventional teaching.