72 citations · 188 across the 26 of their papers we have counts for
8 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…
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…
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…
A generative flow for conditional sampling via optimal transport
Jason Alfonso, Ricardo Baptista, Anupam Bhakta +7
Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation. Generative models, such as normalizing flows and generative adversarial netw…
Learning non-Gaussian graphical models via Hessian scores and triangular transport
Ricardo Baptista, Youssef Marzouk, Rebecca E. Morrison +1
Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables. Knowing the sparsity of such a graphica…
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…
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference
Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki +1
We present a novel framework for conditional sampling of probability measures, using block triangular transport maps. We develop the theoretical foundations of block triangular tra…
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista, Matthias Poloczek
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The com…