AI RESEARCH
Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
arXiv CS.LG
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ArXi:2605.06172v1 Announce Type: cross Many normalizing flow architectures impose regularity constraints, yet their distributional approximation properties are not fully characterized. We study the expressivity of bi-Lipschitz normalizing flows through the lens of score-based diffusion models. For the probability flow ODE of a variance-preserving diffusion, Lipschitz regularity of the score induces a flow of bi-Lipschitz diffeomorphic transport maps.