论文标题
数据幻觉的迭代教学
Iterative Teaching by Data Hallucination
论文作者
论文摘要
我们考虑了迭代机械教学的问题，其中教师根据学习者在离散输入空间（即有限样本池）下的学习者的状态顺序提供示例，这极大地限制了教师的能力。为了解决这个问题，我们在连续的输入空间下研究迭代教学，其中输入示例（即图像）可以通过解决优化问题或直接从连续分布中得出。具体来说，我们提出了数据幻觉教学（DHT），其中教师可以根据标签，学习者的状态和目标概念智能地生成输入数据。我们研究了许多具有挑战性的教学设置（例如，无所不知和黑色盒子设置中的线性/神经学习者）。广泛的经验结果验证了DHT的有效性。
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.
