June 7, 2024, 4:44 a.m. | Behrooz Tahmasebi, Stefanie Jegelka

cs.LG updates on arXiv.org arxiv.org

arXiv:2311.02868v2 Announce Type: replace
Abstract: Group-invariant probability distributions appear in many data-generative models in machine learning, such as graphs, point clouds, and images. In practice, one often needs to estimate divergences between such distributions. In this work, we study how the inherent invariances, with respect to any smooth action of a Lie group on a manifold, improve sample complexity when estimating the 1-Wasserstein distance, the Sobolev Integral Probability Metrics (Sobolev IPMs), the Maximum Mean Discrepancy (MMD), and also the complexity …

abstract action arxiv complexity cs.lg data generative generative models graphs images machine machine learning practice probability replace sample study type work

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