Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency

Ziv Goldfeld, Kristjan Greenewald
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:3327-3337, 2020.

Abstract

Optimal transport (OT), and in particular the Wasserstein distance, has seen a surge of interest and applications in machine learning. However, empirical approximation under Wasserstein distances suffers from a severe curse of dimensionality, rendering them impractical in high dimensions. As a result, entropically regularized OT has become a popular workaround. However, while it enjoys fast algorithms and better statistical properties, it looses the metric structure that Wasserstein distances enjoy. This work proposes a novel Gaussian-smoothed OT (GOT) framework, that achieves the best of both worlds: preserving the 1-Wasserstein metric structure while alleviating the empirical approximation curse of dimensionality. Furthermore, as the Gaussian-smoothing parameter shrinks to zero, GOT $\Gamma$-converges towards classic OT (with convergence of optimizers), thus serving as a natural extension. An empirical study that validates the theoretical results is provided, promoting Gaussian-smoothed OT as a powerful alternative to entropic OT.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-goldfeld20a, title = {Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency}, author = {Goldfeld, Ziv and Greenewald, Kristjan}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {3327--3337}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/goldfeld20a/goldfeld20a.pdf}, url = { http://proceedings.mlr.press/v108/goldfeld20a.html }, abstract = {Optimal transport (OT), and in particular the Wasserstein distance, has seen a surge of interest and applications in machine learning. However, empirical approximation under Wasserstein distances suffers from a severe curse of dimensionality, rendering them impractical in high dimensions. As a result, entropically regularized OT has become a popular workaround. However, while it enjoys fast algorithms and better statistical properties, it looses the metric structure that Wasserstein distances enjoy. This work proposes a novel Gaussian-smoothed OT (GOT) framework, that achieves the best of both worlds: preserving the 1-Wasserstein metric structure while alleviating the empirical approximation curse of dimensionality. Furthermore, as the Gaussian-smoothing parameter shrinks to zero, GOT $\Gamma$-converges towards classic OT (with convergence of optimizers), thus serving as a natural extension. An empirical study that validates the theoretical results is provided, promoting Gaussian-smoothed OT as a powerful alternative to entropic OT.} }
Endnote
%0 Conference Paper %T Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency %A Ziv Goldfeld %A Kristjan Greenewald %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-goldfeld20a %I PMLR %P 3327--3337 %U http://proceedings.mlr.press/v108/goldfeld20a.html %V 108 %X Optimal transport (OT), and in particular the Wasserstein distance, has seen a surge of interest and applications in machine learning. However, empirical approximation under Wasserstein distances suffers from a severe curse of dimensionality, rendering them impractical in high dimensions. As a result, entropically regularized OT has become a popular workaround. However, while it enjoys fast algorithms and better statistical properties, it looses the metric structure that Wasserstein distances enjoy. This work proposes a novel Gaussian-smoothed OT (GOT) framework, that achieves the best of both worlds: preserving the 1-Wasserstein metric structure while alleviating the empirical approximation curse of dimensionality. Furthermore, as the Gaussian-smoothing parameter shrinks to zero, GOT $\Gamma$-converges towards classic OT (with convergence of optimizers), thus serving as a natural extension. An empirical study that validates the theoretical results is provided, promoting Gaussian-smoothed OT as a powerful alternative to entropic OT.
APA
Goldfeld, Z. & Greenewald, K.. (2020). Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:3327-3337 Available from http://proceedings.mlr.press/v108/goldfeld20a.html .

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