Nonparametric Teaching for Multiple Learners

Chen Zhang; Xiaofeng Cao; Weiyang Liu; Ivor Tsang; James Kwok

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arXiv · 2311.10318

Nonparametric Teaching for Multiple Learners

Chen Zhang·Xiaofeng Cao·Weiyang Liu·Ivor Tsang·James Kwok

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Abstract

We study the problem of teaching multiple learners simultaneously in the nonparametric iterative teaching setting, where the teacher iteratively provides examples to the learner for accelerating the acquisition of a target concept. This problem is motivated by the gap between current single-learner teaching setting and the real-world scenario of human instruction where a teacher typically imparts knowledge to multiple students. Under the new problem formulation, we introduce a novel framework -- Multi-learner Nonparametric Teaching (MINT). In MINT, the teacher aims to instruct multiple learners, with each learner focusing on learning a scalar-valued target model. To achieve this, we frame the problem as teaching a vector-valued target model and extend the target model space from a scalar-valued reproducing kernel Hilbert space used in single-learner scenarios to a vector-valued space. Furthermore, we demonstrate that MINT offers significant teaching speed-up over repeated single-learner teaching, particularly when the multiple learners can communicate with each other. Lastly, we conduct extensive experiments to validate the practicality and efficiency of MINT.

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BibTeX RIS

Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, James Kwok. 2023-11-17. Nonparametric Teaching for Multiple Learners. https://arxiv.org/abs/2311.10318

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