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Automatic Differentiation Variational Inference

Author(s): Kucukelbir, Alp; Tran, Dustin; Ranganath, Rajesh; Gelman, Andrew; Blei, David M

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dc.contributor.authorKucukelbir, Alp-
dc.contributor.authorTran, Dustin-
dc.contributor.authorRanganath, Rajesh-
dc.contributor.authorGelman, Andrew-
dc.contributor.authorBlei, David M-
dc.date.accessioned2021-10-08T19:44:23Z-
dc.date.available2021-10-08T19:44:23Z-
dc.date.issued2017en_US
dc.identifier.citationKucukelbir, Alp, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. "Automatic Differentiation Variational Inference." Journal of Machine Learning Research 18, no. 14 (2017): 1-45.en_US
dc.identifier.issn1532-4435-
dc.identifier.issn1533-7928-
dc.identifier.urihttp://www.jmlr.org/papers/v18/16-107.html-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1152s-
dc.description.abstractProbabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop ADVI. Using our method, the scientist only provides a probabilistic model and a dataset, nothing else. ADVI automatically derives an efficient variational inference algorithm, freeing the scientist to refine and explore many models. ADVI supports a broad class of models ---no conjugacy assumptions are required. We study ADVI across ten modern probabilistic models and apply it to a dataset with millions of observations. We deploy ADVI as part of Stan, a probabilistic programming system.en_US
dc.format.extent1 - 45en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of Machine Learning Researchen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleAutomatic Differentiation Variational Inferenceen_US
dc.typeJournal Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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