  We consider the problem of aggregating models learned from sequestered,
possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics,
we develop a general meta-modeling framework that learns shared global latent
structures by identifying correspondences among local model parameterizations.
Our proposed framework is model-independent and is applicable to a wide range
of model types. After verifying our approach on simulated data, we demonstrate
its utility in aggregating Gaussian topic models, hierarchical Dirichlet
process based hidden Markov models, and sparse Gaussian processes with
applications spanning text summarization, motion capture analysis, and
temperature forecasting.
