Machine Teaching

If machine learning is to discover knowledge, then machine teaching is to pass it on.

Machine teaching is an inverse problem to machine learning. Given a learning algorithm and a target model, machine teaching finds an optimal (e.g. the smallest) training set. For example, consider a "student" who runs the Support Vector Machine learning algorithm. Imagine a teacher who wants to teach the student a specific target hyperplane in some feature space (never mind how the teacher got this hyperplane in the first place). The teacher constructs a training set D=(x1,y1) ... (xn, yn), where xi is a feature vector and yi a class label, to train the student. What is the smallest training set that will make the student learn the target hyperplane? It is not hard to see that n=2 is sufficient with the two training items straddling the target hyperplane. Machine teaching mathematically formalizes this idea and generalizes it to many kinds of learning algorithms and teaching targets. Solving the machine teaching problem in general can be intricate and is an open mathematical question, though for a large family of learners the resulting bilevel optimization problem can be approximated.

Machine teaching can have impacts in education, where the "student" is really a human student, and the teacher certainly has a target model (i.e. the educational goal). If we are willing to assume a cognitive learning model of the student, we can use machine teaching to reverse-engineer the optimal training data -- which will be the optimal, personalized lesson for that student. We have shown feasibility in a preliminary cognitive study to teach categorization. Another application is in computer security where the "teacher" is an attacker and the learner is any intelligent system that adapts to inputs.

This page contains our research on the theory, algorithms, and applications of machine teaching.



Theory of Machine Teaching

Applications in Security, Trustworthy, and Interpretable AI

Applications in Human Computer Interaction

Applications in Cognitive Psychology and Education



Code and Data

In the media

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