Author

M. Schmidt

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#machine learning Preprint Sep 2026

Stable Filters for Generative Modeling of Graph Signals

This paper derives explicit Wasserstein stability bounds that quantify the effect of relative graph perturbations on the generated distributions and introduces a principled framework for designing stable graph filters that preserve the smoothing behavior of graph heat diffusion, while boosting structural stability.

M. Schmidt, Gonzalo Mateos · 0 citations

Jul 2026

Stability of Flow Models for Graph Signals

This paper analyzes continuous normalized flow models parameterized by GNNs and shows that permutation equivariance is preserved for both the resulting continuous-time ordinary differential equations and their discrete numerical approximations used as graph signal samplers.

M. Schmidt, Gonzalo Mateos · arXiv.org · 1 citation