Adding a Conditional Value-at-Risk tail penalty to a Lipschitz-regularized divergence creates a bounded non-Lipschitz particle flow that improves heavy-tail accuracy of pre-trained generative models.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Adding a Conditional Value-at-Risk tail penalty to a Lipschitz-regularized divergence creates a bounded non-Lipschitz particle flow that improves heavy-tail accuracy of pre-trained generative models.