Identifiability and Stability of Generative Drifting in the Companion-Elliptic Kernel Family
A drifting model is a one-step generator trained by moving each sample along a field of kernel-weighted attraction toward data samples and repulsion between model samples; training halts once this field vanishes. The soundness of this scheme rests on two questions: whether a zero-field equilibrium guarantees agreement with the data distribution, and how the error is controlled when the field is small. We answer both questions. We introduce the companion-elliptic kernel class, which contains the Laplace kernel, and prove that a vanishing field identifies arbitrary Borel probability measures within this class and its blockwise product extension. The scalar companion-elliptic kernels are exa...
| Authors | HakGeun Lee, Hyonho Chun |
|---|---|
| Primary category | stat.ML |
| Published | 27 April 2026 |
| Last updated at source | 13 August 2026 |
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