Title: Iterative Machine Teaching,
Abstract: In this paper, we consider the problem of machine teaching, the inverse
problem of machine learning. Different from traditional machine teaching which
views the learners as batch algorithms, we study a new paradigm where the
learner uses an iterative algorithm and a teacher can feed examples
sequentially and intelligently based on the current performance of the learner.
We show that the teaching complexity in the iterative case is very different
from that in the batch case. Instead of constructing a minimal training set for
learners, our iterative machine teaching focuses on achieving fast convergence
in the learner model. Depending on the level of information the teacher has
from the learner model, we design teaching algorithms which can provably reduce
the number of teaching examples and achieve faster convergence than learning
without teachers. We also validate our theoretical findings with extensive
experiments on different data distribution and real image datasets.