Understanding the Power and Limitations of Teaching with Imperfect Knowledge
This addresses the problem of designing robust teaching algorithms for real-world applications like education, but it is incremental as it builds on existing machine teaching frameworks.
The paper investigates how a teacher with imperfect knowledge can effectively teach a learner, showing connections between imperfect knowledge and the construction of optimal teaching sets.
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical assumption is that the teacher has perfect knowledge of the task---this knowledge comprises knowing the desired learning target, having the exact task representation used by the learner, and knowing the parameters capturing the learning dynamics of the learner. Inspired by real-world applications of machine teaching in education, we consider the setting where teacher's knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge? We answer this question by showing connections to how imperfect knowledge affects the teacher's solution of the corresponding machine teaching problem when constructing optimal teaching sets. Our results have important implications for designing robust teaching algorithms for real-world applications.