Showing stat.MLShow all
3 papers · 1 filter
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…
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…
stat.ML2024
A competitive baseline for deep learning enhanced data assimilation using conditional Gaussian ensemble Kalman filtering
Zachariah Malik, Romit Maulik
Ensemble Kalman Filtering (EnKF) is a popular technique for data assimilation, with far ranging applications. However, the vanilla EnKF framework is not well-defined when perturbat…