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.

翻译：我们研究了迭代式机器教学问题，其中教师根据学习者在离散输入空间（即有限样本池）中的状态依次提供示例，这极大地限制了教师的能力。为解决这一问题，我们研究了连续输入空间下的迭代式教学，在该空间中输入示例（如图像）既可通过求解优化问题生成，也可直接从连续分布中采样。具体而言，我们提出了数据幻象教学（DHT），教师能够根据标签、学习者状态以及目标概念智能地生成输入数据。我们探究了若干具有挑战性的教学场景（如全知和黑盒设置下的线性/神经网络学习者）。大量实验结果表明DHT的有效性。