Artificial intelligence is to teach machines to take actions like humans. To
achieve intelligent teaching, the machine learning community becomes to think
about a promising topic named machine teaching where the teacher is to design
the optimal (usually minimal) teaching set given a target model and a specific
learner. However, previous works usually require numerous teaching examples
along with large iterations to guide learners to converge, which is costly. In
this paper, we consider a more intelligent teaching paradigm named one-shot
machine teaching which costs fewer examples to converge faster. Different from
typical teaching, this advanced paradigm establishes a tractable mapping from
the teaching set to the model parameter. Theoretically, we prove that this
mapping is surjective, which serves to an existence guarantee of the optimal
teaching set. Then, relying on the surjective mapping from the teaching set to
the parameter, we develop a design strategy of the optimal teaching set under
appropriate settings, of which two popular efficiency metrics, teaching
dimension and iterative teaching dimension are one. Extensive experiments
verify the efficiency of our strategy and further demonstrate the intelligence
of this new teaching paradigm.

本文提出了一种更智能的机器教学范式，即一次性机器教学，并建立了可行的映射方法设计最优化（通常为最小）教学集，以便更快地收敛，从而证明了新教学范式的智能性并验证了该策略的有效性。