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Model Fusion with Kullback--Leibler Divergence

Sebastian Claici · Mikhail Yurochkin · Soumya Ghosh · Justin Solomon

Keywords: [ Approximate Inference ] [ Bayesian Methods ] [ Parallel and Distributed Learning ] [ Probabilistic Inference - Approximate, Monte Carlo, and Spectral Methods ]


We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach. The components of the dataset posteriors are assigned to the proposed global model components by solving a regularized variant of the assignment problem. The global components are then updated based on these assignments by their mean under a KL divergence. For exponential family variational distributions, our formulation leads to an efficient non-parametric algorithm for computing the fused model. Our algorithm is easy to describe and implement, efficient, and competitive with state-of-the-art on motion capture analysis, topic modeling, and federated learning of Bayesian neural networks.

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