Stein Variational Adaptive Importance Sampling

Jun Han, Qiang Liu
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:711-720, 2017.

Abstract

We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverages the nonparametric transforms in SVGD to iteratively decrease the KL divergence between importance proposals and target distributions. The advantages of our algorithm are twofold: 1) it turns SVGD into a standard IS algorithm, allowing us to use standard diagnostic and analytic tools of IS to evaluate and interpret the results, and 2) it does not restrict the choice of the importance proposals to predefined distribution families like traditional (adaptive) IS methods. Empirical experiments demonstrate that our algorithm performs well on evaluating partition functions of restricted Boltzmann machines and testing likelihood of variational auto-encoders.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-han17a, title = {{S}tein Variational Adaptive Importance Sampling}, author = {Han, Jun and Liu, Qiang}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {711--720}, 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/han17a/han17a.pdf}, url = {https://proceedings.mlr.press/r15/han17a.html}, abstract = {We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverages the nonparametric transforms in SVGD to iteratively decrease the KL divergence between importance proposals and target distributions. The advantages of our algorithm are twofold: 1) it turns SVGD into a standard IS algorithm, allowing us to use standard diagnostic and analytic tools of IS to evaluate and interpret the results, and 2) it does not restrict the choice of the importance proposals to predefined distribution families like traditional (adaptive) IS methods. Empirical experiments demonstrate that our algorithm performs well on evaluating partition functions of restricted Boltzmann machines and testing likelihood of variational auto-encoders.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Stein Variational Adaptive Importance Sampling %A Jun Han %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-han17a %I PMLR %P 711--720 %U https://proceedings.mlr.press/r15/han17a.html %V R15 %X We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverages the nonparametric transforms in SVGD to iteratively decrease the KL divergence between importance proposals and target distributions. The advantages of our algorithm are twofold: 1) it turns SVGD into a standard IS algorithm, allowing us to use standard diagnostic and analytic tools of IS to evaluate and interpret the results, and 2) it does not restrict the choice of the importance proposals to predefined distribution families like traditional (adaptive) IS methods. Empirical experiments demonstrate that our algorithm performs well on evaluating partition functions of restricted Boltzmann machines and testing likelihood of variational auto-encoders. %Z Reissued by PMLR on 04 October 2026.
APA
Han, J. & Liu, Q.. (2017). Stein Variational Adaptive Importance Sampling. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:711-720 Available from https://proceedings.mlr.press/r15/han17a.html. Reissued by PMLR on 04 October 2026.

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