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，我们考虑迭代式机器教学问题，其中教师根据当前迭代式学习器的状态顺序提供示例。与以往需要在每次迭代中扫描整个候选池并从中选择教学示例的方法不同，我们提出一种标签合成教学框架：教师随机选取输入教学示例（如图像），然后为其合成合适的输出（如标签）。我们证明该框架既能避免代价高昂的示例选择过程，又能以可证明的方式实现指数级可教性。我们在此框架下提出了多种新颖的教学算法。最后，我们通过实验验证了该框架的有效性。