  Machine teaching is an inverse problem of machine learning that aims at
steering the student learner towards its target hypothesis, in which the
teacher has already known the student's learning parameters. Previous studies
on machine teaching focused on balancing the teaching risk and cost to find
those best teaching examples deriving the student model. This optimization
solver is in general ineffective when the student learner does not disclose any
cue of the learning parameters. To supervise such a teaching scenario, this
paper presents a distribution matching-based machine teaching strategy.
Specifically, this strategy backwardly and iteratively performs the halving
operation on the teaching cost to find a desired teaching set. Technically, our
strategy can be expressed as a cost-controlled optimization process that finds
the optimal teaching examples without further exploring in the parameter
distribution of the student learner. Then, given any a limited teaching cost,
the training examples will be closed-form. Theoretical analysis and experiment
results demonstrate this strategy.
