UC Berkeley

Classically, most machine learning (ML) methodology has made an innocuous modeling assumption: data drawn from both the training/test sets has been independently sampled from a pair of identical distributions with nice properties. Yet, in the situations modern ML methods must confront, deviations from this idealized setting are quickly becoming the norm–not the exception. In this thesis, we address the challenges arising in understanding the often unexpected phenomenology in these settings by developing theory in two areas of interest: transfer learning and robust learning. In particular, we focus on identifying what structural conditions/techniques are needed to permit sample-efficient learning in these new settings, in order to answer questions such as why pretraining is so effective and what the limits of learning are for extremely heavy-tailed distributions.