{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/statistical-model-aggregation-via-parameter","title":"Statistical Model Aggregation via Parameter Matching","arxiv_id":"1911.00218","date":"2019-11-01","proceeding":"NeurIPS 2019 12","authors":["Mikhail Yurochkin","Mayank Agarwal","Soumya Ghosh","Kristjan Greenewald","Trong Nghia Hoang"],"abstract":"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. 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