Skip to main content

Nonparametric variational inference.

Author(s): Gershman, Samuel; Hoffman, Matthew D; Blei, David M

To refer to this page use:
Abstract: Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Unlike most other variational approximations, using multiple kernels allows the approximation to capture multiple modes of the posterior. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and MCMC approximations. The method is easy to apply to graphical models for which standard variational methods are difficult to derive.
Publication Date: 2012
Citation: Gershman, S.J., Hoffman, M.D., & Blei, D.M. (2012). Nonparametric variational inference. In Proceedings of the 29th International Conference on International Conference on Machine Learning (ICML'12) (pp. 235-242).
Pages: 235 - 242
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 29th International Conference on International Conference on Machine Learning
Version: Final published version. This is an open access article.

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.