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Black Box Variational Inference

Author(s): Ranganath, Rajesh; Gerrish, Sean; Blei, David M

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Abstract: Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis. These efforts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. In this paper, we present a “black box” variational inference algorithm, one that can be quickly applied to many models with little additional derivation. Our method is based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the variational distribution. We develop a number of methods to reduce the variance of the gradient, always maintaining the criterion that we want to avoid difficult model-based derivations. We evaluate our method against the corresponding black box sampling based methods. We find that our method reaches better predictive likelihoods much faster than sampling methods. Finally, we demonstrate that Black Box Variational Inference lets us easily explore a wide space of models by quickly constructing and evaluating several models of longitudinal healthcare data.
Publication Date: 2014
Citation: Ranganath, Rajesh, Sean Gerrish, and David M. Blei. "Black Box Variational Inference." Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics 33: pp. 814-822. 2014.
ISSN: 2640-3498
Pages: 814 - 822
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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