Generalizing Score-based generative models for Heavy-tailed Distributions
Score-based generative models (SGMs) have achieved remarkable empirical success, motivating their application to a broad range of data distributions. However, extending them to heavy-tailed targets remains a largely open problem. Although dedicated models for heavy-tailed distributions have been proposed, their generative fidelity remains unclear and they lack solid theoretical foundations, leaving important questions open in this regime. In this paper, we address this gap through two theoretical contributions. First, we show that combining early stopping with a suitable initialization is sufficient to extend the diffusion framework to any target distribution; in particular, we establish ...
| Authors | Tiziano Fassina, Gabriel Cardoso, Sylvan Le Corff et al. |
|---|---|
| Primary category | stat.ML |
| Published | 28 February 2026 |
| Last updated at source | 14 May 2026 |
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