In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.

翻译：在本文中,我们考虑了迭代机教学的问题,在迭代机教学中,教师根据目前的迭代学习者按顺序提供实例。与以前的方法相比,我们建议了一个标签综合教学框架,由教师随机选择教学范例(例如图像),然后合成适当的产出(例如标签)。我们表明,这个框架可以避免费用高昂的范例选择,同时仍然可以实现指数化教学。我们在此框架中提出了多种新的教学算法。最后,我们从经验上展示了框架的价值。