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arXiv Machine Learning By Ziyu Chen, Markos A. Katsoulakis, Benjamin J. Zhang

Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences

Read the original on arXiv Machine Learning →

arXiv:2410. 01244v2 Announce Type: replace-cross Abstract: We introduce a novel Wasserstein-1 ($W_1$) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertainty Propagation (WUP) theorem that bounds the $W_1$ distance between terminal distributions by the proposed divergence, equivalently characterized by a weighted $L^2$ discrepancy between the underlying drifts and the $W_1$ distance between their initial measures.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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