Advances in Variational Inference

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...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 41; H. 8; S. 2008 - 2026
Hauptverfasser: Zhang, Cheng, Butepage, Judith, Kjellstrom, Hedvig, Mandt, Stephan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
approximate Bayesian inference
Bayes methods
Bayesian analysis
Computational modeling
Hidden Markov models
Inference
inference networks
Machine learning
Optimization
Probabilistic logic
Probabilistic models
reparameterization gradients
scalable inference
Stochastic processes
structured variational approximations
Variational inference
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung: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, (c) accurate VI, which includes variational models beyond the 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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2018.2889774