72 citations · 188 across the 26 of their papers we have counts for
7 papers · 1 filter
Computational Optimal Transport and Filtering on Riemannian manifolds
Daniel Grange, Mohammad Al-Jarrah, Ricardo Baptista +4
In this paper we extend recent developments in computational optimal transport to the setting of Riemannian manifolds. In particular, we show how to learn optimal transport maps fr…
An adaptive ensemble filter for heavy-tailed distributions: tuning-free inflation and localization
Mathieu Le Provost, Ricardo Baptista, Jeff D. Eldredge +1
Heavy tails is a common feature of filtering distributions that results from the nonlinear dynamical and observation processes as well as the uncertainty from physical sensors. In…
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…
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Zhong Yi Wan, Ricardo Baptista, Yi-fan Chen +4
We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data…
An Approximation Theory Framework for Measure-Transport Sampling Algorithms
Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki +2
This article presents a general approximation-theoretic framework to analyze measure transport algorithms for probabilistic modeling. A primary motivating application for such algo…
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…