Abstract
This paper introduces a new approach for machine teaching that
partly addresses the (unavoidable) mismatch between what the
teacher assumes about the learning process of the student and the
actual process. We analyze several situations in which such mismatch
takes place, including when the student?s learning algorithm
is known but the corresponding parameters are not, and when the
learning algorithm itself is not known. Our analysis is focused on
the case of a Bayesian Gaussian learner, and we show that, even
in this simple case, the lack of knowledge regarding the student?s
learning process significantly deteriorates the performance of machine
teaching: while perfect knowledge of the student ensures that
the target is learned after a finite number of samples, lack of knowledge
thereof implies that the student will only learn asymptotically
(i.e., after an infinite number of samples). We introduce interactivity
as a means to mitigate the impact of imperfect knowledge
and show that, by using interactivity, we are able to recover finite
learning time, in the best case, or significantly faster convergence,
in the worst case. Finally, we discuss the extension of our analysis
to a classification problem using linear discriminant analysis, and
discuss the implications of our results in single- and multi-student
settings.
Keywords
- Machine Learning: New Problems
- Machine Learning Applications: Applications of Supervised Learning
- Machine Learning Applications: Other Applications
