Model Fusion with Kullback--Leibler Divergence
- URL: http://arxiv.org/abs/2007.06168v1
- Date: Mon, 13 Jul 2020 03:27:45 GMT
- Title: Model Fusion with Kullback--Leibler Divergence
- Authors: Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh and Justin Solomon
- Abstract summary: 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.
- Score: 58.20269014662046
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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