Bayesian Transfer Learning
Piotr M. Suder Jason Xu David B. Dunson
Transfer learning is a burgeoning concept in statistical machine learning
that seeks to improve inference and/or predictive accuracy on a domain of
interest by leveraging data from related domains. While the term "transfer
learning" has garnered much recent interest, its foundational principles have
existed for years under various guises. Prior literature reviews in computer
science and electrical engineering have sought to bring these ideas into focus,
primarily surveying general methodologies and works from these disciplines.
This article highlights Bayesian approaches to transfer learning, which have
received relatively limited attention despite their innate compatibility with
the notion of drawing upon prior knowledge to guide new learning tasks. Our
survey encompasses a wide range of Bayesian transfer learning frameworks
applicable to a variety of practical settings. We discuss how these methods
address the problem of finding the optimal information to transfer between
domains, which is a central question in transfer learning. We illustrate the
utility of Bayesian transfer learning methods via a simulation study where we
compare performance against frequentist competitors.