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Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin · Mayank Agarwal · Soumya Ghosh · Kristjan Greenewald · Nghia Hoang

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #145

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.

Author Information

Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab)
Mayank Agarwal (IBM Research AI, MIT-IBM Watson AI Lab)
Soumya Ghosh (IBM Research)
Kristjan Greenewald (IBM Research)
Nghia Hoang (IBM Research)

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