4 papers
When do explicit penalties improve flow matching?
Lea Kunkel, Alberto González-Sanz, Sophie Langer
Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation. We study penalties that encode such constraints, with…
Distribution estimation via Flow Matching with Lipschitz guarantees
Lea Kunkel
Flow Matching, a promising approach in generative modeling, has recently gained popularity. Relying on ordinary differential equations, it offers a simple and flexible alternative…
On the minimax optimality of Flow Matching through the connection to kernel density estimation
Lea Kunkel, Mathias Trabs
Flow Matching has recently gained attention in generative modeling as a simple and flexible alternative to diffusion models. While existing statistical guarantees adapt tools from…
A Wasserstein perspective of Vanilla GANs
Lea Kunkel, Mathias Trabs
The empirical success of Generative Adversarial Networks (GANs) caused an increasing interest in theoretical research. The statistical literature is mainly focused on Wasserstein G…