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We adopt a multi-view approach for analyzing two knowledge transfersettings---learning using privileged information (LUPI) and distillation---in acommon framework. Under reasonable assumptions about the complexities ofhypothesis spaces, and being optimistic about the expected loss achievable bythe student (in distillation) and a transformed teacher predictor (in LUPI), weshow that encouraging agreement between the teacher and the student leads toreduced search space. As a result, improved convergence rate can be obtainedwith regularized empirical risk minimization.

Everything old is new again: A multi-view learning approach to learning using privileged information and distillation