Research

Statistical Model Aggregation via Parameter Matching

NeurIPS

Authors

Published on

12/14/2019

Categories

NeurIPS

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.

This work was published in NeurIPS 2019.

Please cite our work using the BibTeX below.

@inproceedings{DBLP:conf/nips/YurochkinAGGH19,
  author={Mikhail Yurochkin and Mayank Agarwal and Soumya Ghosh and Kristjan H. Greenewald and Trong Nghia Hoang},
  title={Statistical Model Aggregation via Parameter Matching},
  year={2019},
  cdate={1546300800000},
  pages={10954-10964},
  url={https://proceedings.neurips.cc/paper/2019/hash/ecb287ff763c169694f682af52c1f309-Abstract.html},
  booktitle={NeurIPS},
  crossref={conf/nips/2019}
}
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