Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
In many high-stakes domains, extreme events carry substantial consequences, yet learning the heavy-tailed distributions that govern them from finite samples remains challenging: the quantities of interest are driven by a few extreme observations, so the tail is under-sampled relative to its importance. Even generative models tailored for heavy tails capture the tail region inadequately in practice. We propose the Conditional Value-at-Risk (CVaR)-penalized Generative Particle Algorithm (CVaR-GPA), a tail-agnostic algorithm for fine-tuning generative models toward heavy-tailed targets, built as a time discretization of the Wasserstein gradient flow of the Lipschitz-regularized KL divergence...
| Authors | Thejani Gamage, Hyemin Gu, Zhizhen Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 August 2026 |
| Last updated at source | 1 October 2026 |
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