  In real-world applications of education, an effective teacher adaptively
chooses the next example to teach based on the learner's current state.
However, most existing work in algorithmic machine teaching focuses on the
batch setting, where adaptivity plays no role. In this paper, we study the case
of teaching consistent, version space learners in an interactive setting. At
any time step, the teacher provides an example, the learner performs an update,
and the teacher observes the learner's new state. We highlight that adaptivity
does not speed up the teaching process when considering existing models of
version space learners, such as "worst-case" (the learner picks the next
hypothesis randomly from the version space) and "preference-based" (the learner
picks hypothesis according to some global preference). Inspired by human
teaching, we propose a new model where the learner picks hypotheses according
to some local preference defined by the current hypothesis. We show that our
model exhibits several desirable properties, e.g., adaptivity plays a key role,
and the learner's transitions over hypotheses are smooth/interpretable. We
develop efficient teaching algorithms and demonstrate our results via
simulation and user studies.

Machine Learning