Variational Boosting: Iteratively Refining Posterior Approximations

@InProceedings{pmlr-v70-miller17a,
  title = 	 {Variational Boosting: Iteratively Refining Posterior Approximations},
  author =       {Andrew C. Miller and Nicholas J. Foti and Ryan P. Adams},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {2420--2429},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/miller17a/miller17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/miller17a.html},
  abstract = 	 {We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing a trade-off between computation time and accuracy. We expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that the resulting posterior inferences compare favorably to existing variational algorithms.}
}