  Learning often involves interaction between multiple agents. Human
teacher-student settings best illustrate how interactions result in efficient
knowledge passing where the teacher constructs a curriculum based on their
students' abilities. Prior work in machine teaching studies how the teacher
should construct optimal teaching datasets assuming the teacher knows
everything about the student. However, in the real world, the teacher doesn't
have complete information about the student. The teacher must interact and
diagnose the student, before teaching. Our work proposes a simple diagnosis
algorithm which uses Gaussian processes for inferring student-related
information, before constructing a teaching dataset. We apply this to two
settings. One is where the student learns from scratch and the teacher must
figure out the student's learning algorithm parameters, eg. the regularization
parameters in ridge regression or support vector machines. Two is where the
student has partially explored the environment and the teacher must figure out
the important areas the student has not explored; we study this in the offline
reinforcement learning setting where the teacher must provide demonstrations to
the student and avoid sending redundant trajectories. Our experiments highlight
the importance of diagosing before teaching and demonstrate how students can
learn more efficiently with the help of an interactive teacher. We conclude by
outlining where diagnosing combined with teaching would be more desirable than
passive learning.
