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Advances in Variational Inference
Microsoft Res, Cambridge CB1 2FB, England..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-5750-9655
2019 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 41, no 8, p. 2008-2026Article in journal (Refereed) Published
Abstract [en]

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2019. Vol. 41, no 8, p. 2008-2026
Keywords [en]
Variational inference, approximate Bayesian inference, reparameterization gradients, structured variational approximations, scalable inference, inference networks
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-255405DOI: 10.1109/TPAMI.2018.2889774ISI: 000473598800016PubMedID: 30596568Scopus ID: 2-s2.0-85059288228OAI: oai:DiVA.org:kth-255405DiVA, id: diva2:1342838
Note

QC 20190814

Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved

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Butepage, JudithKjellström, Hedvig

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