Truncation-Free Online Variational Inference for Bayesian Nonparametric Models
Abstract
We present a truncation-free online variational inference algorithm for Bayesian nonparametric models. Unlike traditional (online) variational inference algorithms that require truncations for the model or the variational distribution, our method adapts model complexity on the fly. Our experiments for Dirichlet process mixture models and hierarchical Dirichlet process topic models on two large-scale data sets show better performance than previous online variational inference algorithms.
Cite
Text
Wang and Blei. "Truncation-Free Online Variational Inference for Bayesian Nonparametric Models." Neural Information Processing Systems, 2012.Markdown
[Wang and Blei. "Truncation-Free Online Variational Inference for Bayesian Nonparametric Models." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/wang2012neurips-truncationfree/)BibTeX
@inproceedings{wang2012neurips-truncationfree,
title = {{Truncation-Free Online Variational Inference for Bayesian Nonparametric Models}},
author = {Wang, Chong and Blei, David M.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {413-421},
url = {https://mlanthology.org/neurips/2012/wang2012neurips-truncationfree/}
}