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Machine teaching studies the interaction between a teacher and astudent/learner where the teacher selects training examples for the learner tolearn a specific task. The typical assumption is that the teacher has perfectknowledge of the task---this knowledge comprises knowing the desired learningtarget, having the exact task representation used by the learner, and knowingthe parameters capturing the learning dynamics of the learner. Inspired byreal-world applications of machine teaching in education, we consider thesetting where teacher's knowledge is limited and noisy, and the key researchquestion we study is the following: When does a teacher succeed or fail ineffectively teaching a learner using its imperfect knowledge? We answer thisquestion by showing connections to how imperfect knowledge affects theteacher's solution of the corresponding machine teaching problem whenconstructing optimal teaching sets. Our results have important implications fordesigning robust teaching algorithms for real-world applications.

Understanding the Power and Limitations of Teaching with Imperfect Knowledge