Iterative Machine Teaching without Teachers
This work addresses the challenge of teaching in unsupervised settings where true labels are unavailable, offering an incremental improvement by adapting existing crowdsourcing techniques to machine teaching.
The paper tackles the problem of iterative machine teaching without access to true labels by applying crowdsourcing methods to estimate labels and student models, enabling collaborative learning without teachers. Experimental results show the method is particularly effective for low-level students.
Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on iterative machine teaching are based on supervised machine learning and assume that there are teachers who know the true answers of all teaching examples. In this study, we consider an unsupervised case where such teachers do not exist; that is, we cannot access the true answer of any teaching example. Students are given a teaching example at each iteration, but there is no guarantee if the corresponding label is correct. Recent studies on crowdsourcing have developed methods for estimating the true answers from crowdsourcing responses. In this study, we apply these to iterative machine teaching for estimating the true labels of teaching examples along with student models that are used for teaching. Our method supports the collaborative learning of students without teachers. The experimental results show that the teaching performance of our method is particularly effective for low-level students in particular.