4 papers · 1 filter
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Markus Heinonen, Yair Shenfeld, Ricardo Baptista +4
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distribu…
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
Zheyu Oliver Wang, Ricardo Baptista, Youssef Marzouk +2
We present two neural network approaches that approximate the solutions of static and dynamic $\unicode{x1D450}\unicode{x1D45C}\unicode{x1D45B}\unicode{x1D451}\unicode{x1D456}\unic…
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan +2
Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promisin…
On the representation and learning of monotone triangular transport maps
Ricardo Baptista, Youssef Marzouk, Olivier Zahm
Transportation of measure provides a versatile approach for modeling complex probability distributions, with applications in density estimation, Bayesian inference, generative mode…