Iterative Classroom Teaching

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O (min{d,N} log 1/eps) examples, where d is the ambient dimension of the problem, N is the number of learners, and eps is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners' internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners' workload and the teacher's cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.


Publié dans:
33rd AAAI Conference on Artificial Intelligence
Présenté à:
33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, January 27 – February 1, 2019
Année
2019
Laboratoires:




 Notice créée le 2019-03-22, modifiée le 2019-03-31

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