Nonparametric Teaching for Multiple Learners
Chen Zhang Xiaofeng Cao Weiyang Liu Ivor Tsang James Kwok
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.