Learning to Draw Samples with Amortized Stein Variational Gradient Descent

Yihao Feng, Dilin Wang, Qiang Liu
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:521-530, 2017.

Abstract

We propose a simple algorithm to train stochas- tic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient di- rection (Liu & Wang, 2016) that maximally de- creases the KL divergence with the target distri- bution. Our method works for any target dis- tribution specified by their unnormalized density function, and can train any black-box architec- tures that are differentiable in terms of the pa- rameters we want to adapt. We demonstrate our method with a number of applications, including variational autoencoder (VAE) with expressive encoders to model complex latent space struc- tures, and hyper-parameter learning of MCMC samplers that allows Bayesian inference to adap- tively improve itself when seeing more data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-feng17a, title = {Learning to Draw Samples with Amortized {S}tein Variational Gradient Descent}, author = {Feng, Yihao and Wang, Dilin and Liu, Qiang}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {521--530}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/feng17a/feng17a.pdf}, url = {https://proceedings.mlr.press/r15/feng17a.html}, abstract = {We propose a simple algorithm to train stochas- tic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient di- rection (Liu & Wang, 2016) that maximally de- creases the KL divergence with the target distri- bution. Our method works for any target dis- tribution specified by their unnormalized density function, and can train any black-box architec- tures that are differentiable in terms of the pa- rameters we want to adapt. We demonstrate our method with a number of applications, including variational autoencoder (VAE) with expressive encoders to model complex latent space struc- tures, and hyper-parameter learning of MCMC samplers that allows Bayesian inference to adap- tively improve itself when seeing more data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning to Draw Samples with Amortized Stein Variational Gradient Descent %A Yihao Feng %A Dilin Wang %A Qiang Liu %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-feng17a %I PMLR %P 521--530 %U https://proceedings.mlr.press/r15/feng17a.html %V R15 %X We propose a simple algorithm to train stochas- tic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient di- rection (Liu & Wang, 2016) that maximally de- creases the KL divergence with the target distri- bution. Our method works for any target dis- tribution specified by their unnormalized density function, and can train any black-box architec- tures that are differentiable in terms of the pa- rameters we want to adapt. We demonstrate our method with a number of applications, including variational autoencoder (VAE) with expressive encoders to model complex latent space struc- tures, and hyper-parameter learning of MCMC samplers that allows Bayesian inference to adap- tively improve itself when seeing more data. %Z Reissued by PMLR on 04 October 2026.
APA
Feng, Y., Wang, D. & Liu, Q.. (2017). Learning to Draw Samples with Amortized Stein Variational Gradient Descent. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:521-530 Available from https://proceedings.mlr.press/r15/feng17a.html. Reissued by PMLR on 04 October 2026.

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