3 papers
stat.ML2026
Generative Modeling by Minimizing the Wasserstein-2 Loss
Yu-Jui Huang, Zachariah Malik
This paper develops a generative model by minimizing the second-order Wasserstein loss (the loss) through a distribution-dependent ordinary differential equation (ODE), whose…
math.OC2025
Mean-Field Langevin Diffusions with Density-dependent Temperature
Yu-Jui Huang, Zachariah Malik
In the context of non-convex optimization, we let the temperature of a Langevin diffusion to depend on the diffusion's own density function. The rationale is that the induced densi…
stat.ML2025
A Differential Equation Approach for Wasserstein GANs and Beyond
Zachariah Malik, Yu-Jui Huang
This paper proposes a new theoretical lens to view Wasserstein generative adversarial networks (WGANs). To minimize the Wasserstein-1 distance between the true data distribution an…