7 papers
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Thejani Gamage, Hyemin Gu, Zhizhen Zhang +3
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 capt…
Concentration Inequalities and UQ Bounds for Hypocoercive MCMC Samplers
Jeremiah Birrell, Luc Rey-Bellet
In this work we provide performance guarantees for hypocoercive non-reversible MCMC samplers with invariant measure ; our results apply in particular to the Langevin eq…
Robust Generative Learning with Lipschitz-Regularized -Divergences Allows Minimal Assumptions on Target Distributions
Ziyu Chen, Hyemin Gu, Markos A. Katsoulakis +2
This paper demonstrates the robustness of Lipschitz-regularized -divergences as objective functionals in generative modeling, showing they enable stable learning across a wide…
Proximal optimal transport divergences
Ricardo Baptista, Panagiota Birmpa, Markos A. Katsoulakis +2
We introduce the proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal…
Nonlinear denoising score matching for enhanced learning of structured distributions
Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet +2
We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions. Generalizing to a nonlin…
Statistical Guarantees of Group-Invariant GANs
Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet +1
This work presents the first statistical performance guarantees for group-invariant generative models. Many real data, such as images and molecules, are invariant to certain group…