2 papers
cs.LG2025
Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
Andrés Guzmán-Cordero, Felix Dangel, Gil Goldshlager +1
Natural gradient methods significantly accelerate the training of Physics-Informed Neural Networks (PINNs), but are often prohibitively costly. We introduce a suite of techniques t…
cs.LG2025
Exponential Family Variational Flow Matching for Tabular Data Generation
Andrés Guzmán-Cordero, Floor Eijkelboom, Jan-Willem van de Meent
While denoising diffusion and flow matching have driven major advances in generative modeling, their application to tabular data remains limited, despite its ubiquity in real-world…