Heavy-Tailed Flow Matching via Random Clocks

Jul 16, 2026

Researchers introduce Heavy-Tailed Flow Matching via Random Clocks (HTFM), a framework that models heavy-tailed data by representing sources as mixtures of Gaussian distributions conditioned on random clock paths. The method demonstrates improved mode coverage, sample quality, and recovery of tail statistics on imbalanced datasets such as CIFAR10-LT and weather fields, while maintaining efficient sampling. HTFM also enables practical control over the heaviness of generated tails by adjusting the clock law or tail parameter.

Why it matters: This approach offers a principled and practical way to generate and control heavy-tailed distributions, which is important for applications where rare events have significant impact, such as finance and climate modeling.

Full story at: arXiv Statistical ML ↗