Tree Wasserstein distance with weight training
This is the demo code for the paper entitled Approximating 1-Wasserstein Distance with Trees (TMLR 2022)
Note that we used the QuadTree and clustertree implementations of Fixed Support Tree-Sliced Wasserstein Barycenter.
Requirements
Install requirements.
sudo pip install -r requirements.txt
Run
Run example.py
python example.py
Citation
@article{
yamada2022approximating,
title={Approximating 1-Wasserstein Distance with Trees},
author={Makoto Yamada and Yuki Takezawa and Ryoma Sato and Han Bao and Zornitsa Kozareva and Sujith Ravi},
journal={Transactions on Machine Learning Research},
year={2022},
url={https://openreview.net/forum?id=Ig82l87ZVU},
note={}
}
Related papers
- Sho Otao, & Makoto Yamada. A linear time approximation of Wasserstein distance with word embedding selection In EMNLP, 2023.
- Cléa Laouar, Yuki Takezawa, & Makoto Yamada. Large-scale similarity search with Optimal Transport In EMNLP, 2023.
Related Github projects
- Supervised Tree-Wasserstein Distances (ICML 2021)
- Fixed Support Tree-Sliced Wasserstein Barycenter (AISTATS 2022)
Contributors
Name : Makoto Yamada (Okinawa Institute of Science and Technology / Kyoto University) and Yuki Takezawa (Kyoto University)
E-mail : makoto (dot) yamada (at) oist.jp