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In sequential machine teaching, a teacher's objective is to provide theoptimal sequence of inputs to sequential learners in order to guide themtowards the best model. In this paper we extend this setting from currentstatic one-data-set analyses to learners which change their learning algorithmor latent state to improve during learning, and to generalize to new datasets.We introduce a multi-agent formulation in which learners' inner state maychange with the teaching interaction, which affects the learning performance infuture tasks. In order to teach such learners, we propose an optimal controlapproach that takes the future performance of the learner after teaching intoaccount. This provides tools for modelling learners having inner states, andmachine teaching of meta-learning algorithms. Furthermore, we distinguishmanipulative teaching, which can be done by effectively hiding data and alsoused for indoctrination, from more general education which aims to help thelearner become better at generalization and learning in new datasets in theabsence of a teacher.

Teaching to Learn: Sequential Teaching of Agents with Inner States