Stochastic wasserstein barycenters
© 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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/1721.1/137895.2 |
| _version_ | 1811092333705297920 |
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| author | Solomon, Justin Chien, Edward Claici, Sebastian |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Solomon, Justin Chien, Edward Claici, Sebastian |
| author_sort | Solomon, Justin |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T15:16:13Z |
| format | Article |
| id | mit-1721.1/137895.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:16:13Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | mit-1721.1/137895.22021-11-09T18:35:58Z Stochastic wasserstein barycenters Solomon, Justin Chien, Edward Claici, Sebastian Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 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. Army Research Office (Grant W911NF-12- R0011) 2021-11-09T18:35:57Z 2021-11-09T14:55:47Z 2021-11-09T18:35:57Z 2018 2019-07-10T12:24:25Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137895.2 Solomon, Justin, Chien, Edward and Claici, Sebastian. 2018. "Stochastic wasserstein barycenters." en http://proceedings.mlr.press/v80/claici18a/claici18a.pdf Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/octet-stream arXiv |
| spellingShingle | Solomon, Justin Chien, Edward Claici, Sebastian Stochastic wasserstein barycenters |
| title | Stochastic wasserstein barycenters |
| title_full | Stochastic wasserstein barycenters |
| title_fullStr | Stochastic wasserstein barycenters |
| title_full_unstemmed | Stochastic wasserstein barycenters |
| title_short | Stochastic wasserstein barycenters |
| title_sort | stochastic wasserstein barycenters |
| url | https://hdl.handle.net/1721.1/137895.2 |
| work_keys_str_mv | AT solomonjustin stochasticwassersteinbarycenters AT chienedward stochasticwassersteinbarycenters AT claicisebastian stochasticwassersteinbarycenters |