A User's Guide to Sampling Strategies for Sliced Optimal Transport
Machine Learning
2025-06-13 v4 Probability
Abstract
This paper serves as a user's guide to sampling strategies for sliced optimal transport. We provide reminders and additional regularity results on the Sliced Wasserstein distance. We detail the construction methods, generation time complexity, theoretical guarantees, and conditions for each strategy. Additionally, we provide insights into their suitability for sliced optimal transport in theory. Extensive experiments on both simulated and real-world data offer a representative comparison of the strategies, culminating in practical recommendations for their best usage.
Keywords
Cite
@article{arxiv.2502.02275,
title = {A User's Guide to Sampling Strategies for Sliced Optimal Transport},
author = {Keanu Sisouk and Julie Delon and Julien Tierny},
journal= {arXiv preprint arXiv:2502.02275},
year = {2025}
}