Stochastic wasserstein barycenters
Name
1802.05757.pdf
Description
Accepted version
Size
1.13 MB
Format
Unknown
Checksum (MD5)
919d2612a32885e3338a65155afa07d2
Author(s)
Solomon, Justin
Chien, Edward
Claici, Sebastian
Date Issued
2018
Citation
Solomon, Justin, Chien, Edward and Claici, Sebastian. 2018. "Stochastic wasserstein barycenters."
Version
Author's final manuscript
Abstract
© 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved. Wi present a stochastic algorithm to compute the baryccntcr of a set of probability distributions under the Wasscrstcin metric from optimal transport Unlike previous approaches,our method extends to continuous input distributions and allows the support of the baryccntcr to be adjusted in each iteration. VVc tacklc the problem without rcgu- larization, allowing us to rccovcr a much sharper output; We give examples where our algorithm recovers a more meaningful baryccntcr than previous work. Our method is versatile and can be extended to applications such as generating super samples from a given distribution and recovering blue noise approximations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v80/claici18a/claici18a.pdf
