Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics. The method is the Wasserstein gradient...
T. Gamage, Hyemin Gu, Zhizhen Zhang et al.
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