Automatic Differentiation Variational Inference

Alp Kucukelbir, David M. Blei, Andrew Gelman, Rajesh Ranganath, Dustin Tran

Research output: Contribution to journalArticle

Abstract

Probabilistic 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 automatic differentiation variational inference (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.

Original languageEnglish (US)
Pages (from-to)1-45
Number of pages45
JournalJournal of Machine Learning Research
Volume18
StatePublished - Jan 1 2017

Keywords

  • Approximate inference
  • Bayesian inference
  • Probabilistic programming

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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  • Cite this

    Kucukelbir, A., Blei, D. M., Gelman, A., Ranganath, R., & Tran, D. (2017). Automatic Differentiation Variational Inference. Journal of Machine Learning Research, 18, 1-45.