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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 importan
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