Ricardo Baptista | DeepAI

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∙ 07/09/2023

A generative flow for conditional sampling via optimal transport

Sampling conditional distributions is a fundamental task for Bayesian in...

0 Jason Alfonso, et al. ∙

∙ 05/24/2023

Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models

We introduce a two-stage probabilistic framework for statistical downsca...

0 Zhong Yi Wan, et al. ∙

∙ 02/27/2023

An Approximation Theory Framework for Measure-Transport Sampling Algorithms

This article presents a general approximation-theoretic framework to ana...

0 Ricardo Baptista, et al. ∙

∙ 02/14/2023

Score-based Diffusion Models in Function Space

Diffusion models have recently emerged as a powerful framework for gener...

0 Jae Hyun Lim, et al. ∙

∙ 10/31/2022

Ensemble transport smoothing – Part 2: nonlinear updates

Smoothing is a specialized form of Bayesian inference for state-space mo...

0 Maximilian Ramgraber, et al. ∙

∙ 10/31/2022

Ensemble transport smoothing – Part 1: unified framework

Smoothers are algorithms for Bayesian time series re-analysis. Most oper...

0 Maximilian Ramgraber, et al. ∙

∙ 07/18/2022

Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective

We consider the problem of reducing the dimensions of parameters and dat...

0 Ricardo Baptista, et al. ∙

∙ 06/22/2022

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

We consider the Bayesian calibration of models describing the phenomenon...

6 Ricardo Baptista, et al. ∙

∙ 03/10/2022

A low-rank ensemble Kalman filter for elliptic observations

We propose a regularization method for ensemble Kalman filtering (EnKF) ...

0 Mathieu Le Provost, et al. ∙

∙ 07/08/2021

Diagonal Nonlinear Transformations Preserve Structure in Covariance and Precision Matrices

For a multivariate normal distribution, the sparsity of the covariance a...

0 Rebecca E. Morrison, et al. ∙

∙ 01/08/2021

Learning non-Gaussian graphical models via Hessian scores and triangular transport

Undirected probabilistic graphical models represent the conditional depe...

0 Ricardo Baptista, et al. ∙

∙ 09/22/2020

An adaptive transport framework for joint and conditional density estimation

We propose a general framework to robustly characterize joint and condit...

0 Ricardo Baptista, et al. ∙

∙ 06/11/2020

Conditional Sampling With Monotone GANs

We present a new approach for sampling conditional measures that enables...

0 Nikola Kovachki, et al. ∙

∙ 06/30/2019

Coupling techniques for nonlinear ensemble filtering

We consider filtering in high-dimensional non-Gaussian state-space model...

0 Alessio Spantini, et al. ∙

∙ 06/22/2018

Bayesian Optimization of Combinatorial Structures

The optimization of expensive-to-evaluate black-box functions over combi...

0 Ricardo Baptista, et al. ∙

∙ 11/02/2017

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting

We present an algorithm to identify sparse dependence structure in conti...

0 Rebecca E. Morrison, et al. ∙

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