A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:745-754, 2018.

Abstract

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algo- rithm simulates samples from a discrete-time Markov chain to approximate a target distribu- tion, thus samples could be highly correlated, an undesired property for SG-MCMC. In con- trary, SVGD directly optimizes a set of particles to approximate a target distribution, and thus is able to obtain good approximations with rela- tively much fewer samples. In this paper, we propose a principle particle-optimization frame- work based on Wasserstein gradient flows to unify SG-MCMC and SVGD, and to allow new algorithms to be developed. Our framework interprets SG-MCMC as particle optimization on the space of probability measures, revealing a strong connection between SG-MCMC and SVGD. The key component of our framework is several particle-approximate techniques to efficiently solve the original partial differential equations on the space of probability measures. Extensive experiments on both synthetic data and deep neural networks demonstrate the ef- fectiveness and efficiency of our framework for scalable Bayesian sampling.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-chen18a, title = {A Unified Particle-Optimization Framework for Scalable {B}ayesian Sampling}, author = {Chen, Changyou and Zhang, Ruiyi and Wang, Wenlin and Li, Bai and Chen, Liqun}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {745--754}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/chen18a/chen18a.pdf}, url = {https://proceedings.mlr.press/r16/chen18a.html}, abstract = {There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algo- rithm simulates samples from a discrete-time Markov chain to approximate a target distribu- tion, thus samples could be highly correlated, an undesired property for SG-MCMC. In con- trary, SVGD directly optimizes a set of particles to approximate a target distribution, and thus is able to obtain good approximations with rela- tively much fewer samples. In this paper, we propose a principle particle-optimization frame- work based on Wasserstein gradient flows to unify SG-MCMC and SVGD, and to allow new algorithms to be developed. Our framework interprets SG-MCMC as particle optimization on the space of probability measures, revealing a strong connection between SG-MCMC and SVGD. The key component of our framework is several particle-approximate techniques to efficiently solve the original partial differential equations on the space of probability measures. Extensive experiments on both synthetic data and deep neural networks demonstrate the ef- fectiveness and efficiency of our framework for scalable Bayesian sampling.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Unified Particle-Optimization Framework for Scalable Bayesian Sampling %A Changyou Chen %A Ruiyi Zhang %A Wenlin Wang %A Bai Li %A Liqun Chen %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-chen18a %I PMLR %P 745--754 %U https://proceedings.mlr.press/r16/chen18a.html %V R16 %X There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algo- rithm simulates samples from a discrete-time Markov chain to approximate a target distribu- tion, thus samples could be highly correlated, an undesired property for SG-MCMC. In con- trary, SVGD directly optimizes a set of particles to approximate a target distribution, and thus is able to obtain good approximations with rela- tively much fewer samples. In this paper, we propose a principle particle-optimization frame- work based on Wasserstein gradient flows to unify SG-MCMC and SVGD, and to allow new algorithms to be developed. Our framework interprets SG-MCMC as particle optimization on the space of probability measures, revealing a strong connection between SG-MCMC and SVGD. The key component of our framework is several particle-approximate techniques to efficiently solve the original partial differential equations on the space of probability measures. Extensive experiments on both synthetic data and deep neural networks demonstrate the ef- fectiveness and efficiency of our framework for scalable Bayesian sampling. %Z Reissued by PMLR on 04 October 2026.
APA
Chen, C., Zhang, R., Wang, W., Li, B. & Chen, L.. (2018). A Unified Particle-Optimization Framework for Scalable Bayesian Sampling. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:745-754 Available from https://proceedings.mlr.press/r16/chen18a.html. Reissued by PMLR on 04 October 2026.

Related Material