Teaching to Learn: Sequential Teaching of Learners with Internal States

Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. However, this teaching objective considers a restricted class of learners with fixed inductive biases. In this paper, we extend the machine teaching framework to learners that can improve their inductive biases, represented as latent internal states, in order to generalize to new datasets. We introduce a novel framework in which learners' inductive biases may change with the teaching interaction, which affects the learning performance in future tasks. In order to teach such learners, we propose a multi-objective control approach that takes the future performance of the learner after teaching into account. This framework provides tools for modelling learners with internal states, humans and meta-learning algorithms alike. Furthermore, we distinguish manipulative teaching, which can be done by effectively hiding data and also used for indoctrination, from teaching to learn which aims to help the learner become better at learning from new datasets in the absence of a teacher. Our empirical results demonstrate that our framework is able to reduce the number of required tasks for online meta-learning, and increases independent learning performance of simulated human users in future tasks.

Original languageEnglish
Title of host publicationAAAI-23 Technical Tracks 5
PublisherAAAI Press
Number of pages9
ISBN (Electronic)9781577358800
Publication statusPublished - 27 Jun 2023
EventAAAI Conference on Artificial Intelligence - Walter E. Washington Convention Center
Duration: 7 Feb 202314 Feb 2023


ConferenceAAAI Conference on Artificial Intelligence


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