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arXiv · 2210.17467

Iterative Teaching by Data Hallucination

Zeju Qiu·Weiyang Liu·Tim Z. Xiao·Zhen Liu·Umang Bhatt·Yucen Luo·Adrian Weller·Bernhard Schölkopf
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Abstract

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability. To address this issue, we study iterative teaching under a continuous input space where the input example (i.e., image) can be either generated by solving an optimization problem or drawn directly from a continuous distribution. Specifically, we propose data hallucination teaching (DHT) where the teacher can generate input data intelligently based on labels, the learner's status and the target concept. We study a number of challenging teaching setups (e.g., linear/neural learners in omniscient and black-box settings). Extensive empirical results verify the effectiveness of DHT.

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Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf. 2022-10-31. Iterative Teaching by Data Hallucination. https://arxiv.org/abs/2210.17467

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