Bayesian Transfer Learning for Emerging Forced Migration Crises

Music City Center 

Transfer learning allows an analyst to borrow information from plentiful historical datasets to improve model behavior on a limited target dataset. Incumbent transfer learning methods proceed by 1) estimating baseline parameters using the entire combined data before 2) fitting parameters to the target dataset while penalizing their difference from the baseline parameters. However, the fact that the baseline parameters depend on the data precludes their use for prior parameterization in the formal Bayesian setting. In this talk, we will instead propose to parameterize the prior via the expected log-likelihood maximizer under a given parameter setting, which removes the data-dependence from the prior. We show how to deploy this prior to conduct Bayesian model selection for determining which historical datasets are relevant to the problem at hand in the generic likelihood setting, including correlated error structures. We also discuss computational aspects of posterior simulation via convex optimization and our motivating application of emerging forced human migration crises.

Transfer Learning

Bayesian Inference

Model Selection