Nonparametric Teaching for Multiple Learners

• URL: http://arxiv.org/abs/2311.10318v1
• Date: Fri, 17 Nov 2023 04:04:11 GMT
• Title: Nonparametric Teaching for Multiple Learners
• Authors: Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, James Kwok
• Abstract summary: We introduce a novel framework -- Multi-learner Nonparametric Teaching (MINT) MINT aims to instruct multiple learners, with each learner focusing on learning a scalar-valued target model. We demonstrate that MINT offers significant teaching speed-up over repeated single-learner teaching.
• Score: 20.75580803325611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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