Bayesian Transfer Learning: Recent Developments, Challenges, and Future Work

Music City Center 

Statistical transfer learning has gained interest among statisticians and data scientists in the recent years. While the idea of using a priori knowledge is not new in statistics, transfer learning formalizes the concept of leveraging information from related data domains in order to improve modeling and prediction for a given learning task. Statisticians have explored the ideas of transfer learning with focus on improving inference and prediction and to this end, they have made substantial contributions to the literature. In this talk, after a review of statistical transfer learning with a focus on Bayesian approaches, we will discuss the recent advances in the literature with focus on papers presented at this session. We will also discuss challenges and future work in this area.

Hierarchical Models