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arXiv 提交日期: 2026-04-27
📄 Abstract - Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families

This paper analyzes identifiability and stability for the drifting field underlying distributional matching in the Generative Drifting framework of Deng et al. First, we introduce the class of companion-elliptic kernels, which includes the Laplace kernel and is characterized by a second-order elliptic coupling between each kernel $\kappa$ in this class and its companion function $\eta$. For each kernel in this class and each pair of Borel probability measures, we prove that the drifting field vanishes if and only if the two probability measures are equal. We further show that this class consists precisely of Gaussian kernels and Matérn kernels with $\nu \ge 1/2$. Second, by constructing counterexamples, we exhibit sequences for which mass escapes to infinity while the field tends to zero; in particular, control of the field norm alone does not guarantee weak convergence. Nevertheless, we prove that the only possible mode of failure is confined to the one-dimensional ray $\{c\,p:0\le c\le 1\}$. Consequently, weak convergence can be restored by imposing an asymptotic lower bound on the intrinsic overlap scalar, a linear observable defined by the kernel and the target measure.

顶级标签: machine learning theory
详细标签: identifiability stability kernel methods generative drifting weak convergence 或 搜索:

伴随椭圆核族生成漂移的可辨识性与稳定性 / Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families


1️⃣ 一句话总结

本文研究了一类称为“伴随椭圆核”的核函数族(包括拉普拉斯核、高斯核和马特恩核),证明其生成的概率场能够惟一识别两个分布是否相同,并发现场趋于零时质量可能向无穷远处逃逸,但通过引入一个与核和目标分布相关的线性标量约束就能恢复弱收敛性。

源自 arXiv: 2604.24196