Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoAAAI-23 Technical Tracks 5
ToimittajatBrian Williams, Yiling Chen, Jennifer Neville
KustantajaAAAI Press
Sivut5939-5947
Sivumäärä9
ISBN (elektroninen)978-1-57735-880-0
DOI - pysyväislinkit
TilaJulkaistu - 27 kesäk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaAAAI Conference on Artificial Intelligence - Walter E. Washington Convention Center, Washington, Yhdysvallat
Kesto: 7 helmik. 202314 helmik. 2023
Konferenssinumero: 37
https://aaai-23.aaai.org/

Julkaisusarja

NimiProceedings of the AAAI Conference on Artificial Intelligence
Numero5
Vuosikerta37
ISSN (elektroninen)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence
LyhennettäAAAI
Maa/AlueYhdysvallat
KaupunkiWashington
Ajanjakso07/02/202314/02/2023
www-osoite

Sormenjälki

Sukella tutkimusaiheisiin 'Teaching to Learn: Sequential Teaching of Learners with Internal States'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

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