  We aim to create a framework for transfer learning using latent factor models
to learn the dependence structure between a larger source dataset and a target
dataset. The methodology is motivated by our goal of building a risk-assessment
model for surgery patients, using both institutional and national surgical
outcomes data. The national surgical outcomes data is collected through NSQIP
(National Surgery Quality Improvement Program), a database housing almost 4
million patients from over 700 different hospitals. We build a latent factor
model with a hierarchical prior on the loadings matrix to appropriately account
for the different covariance structure in our data. We extend this model to
handle more complex relationships between the populations by deriving a scale
mixture formulation using stick-breaking properties. Our model provides a
transfer learning framework that utilizes all information from both the source
and target data, while modeling the underlying inherent differences between
them.
