  We adopt a multi-view approach for analyzing two knowledge transfer
settings---learning using privileged information (LUPI) and distillation---in a
common framework. Under reasonable assumptions about the complexities of
hypothesis spaces, and being optimistic about the expected loss achievable by
the student (in distillation) and a transformed teacher predictor (in LUPI), we
show that encouraging agreement between the teacher and the student leads to
reduced search space. As a result, improved convergence rate can be obtained
with regularized empirical risk minimization.
