3 papers
stat.ML2026
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
Benjamin Sterling, Mónica F. Bugallo, Tom Tirer
Diffusion/score-based models have emerged as powerful generative models, capable of generating high-quality samples that mimic the training data distribution. However, it has been…
cs.LG2026
Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
Benjamin Sterling, Yousef El-Laham, Mónica F. Bugallo
Recent advances in generative artificial intelligence applications have raised new data security concerns. This paper focuses on defending diffusion models against membership infer…
stat.ML2025
Critically-Damped Higher-Order Langevin Dynamics for Generative Modeling
Benjamin Sterling, Chad Gueli, Mónica F. Bugallo
Denoising diffusion probabilistic models (DDPMs) represent an entirely new class of generative AI methods that have yet to be fully explored. They use Langevin dynamics, represente…