Title: Teacher Improves Learning by Selecting a Training Subset,
Abstract: We call a learner super-teachable if a teacher can trim down an iid training
set while making the learner learn even better. We provide sharp super-teaching
guarantees on two learners: the maximum likelihood estimator for the mean of a
Gaussian, and the large margin classifier in 1D. For general learners, we
provide a mixed-integer nonlinear programming-based algorithm to find a super
teaching set. Empirical experiments show that our algorithm is able to find
good super-teaching sets for both regression and classification problems.