Youssef Marzouk

research

∙ 09/03/2023

Distribution learning via neural differential equations: a nonparametric statistical perspective

Ordinary differential equations (ODEs), via their induced flow maps, pro...

0 Youssef Marzouk, et al. ∙

research

∙ 08/26/2023

A transport approach to sequential simulation-based inference

We present a new transport-based approach to efficiently perform sequent...

0 Paul-Baptiste Rubio, et al. ∙

research

∙ 08/22/2023

Evaluating the accuracy of Gaussian approximations in VSWIR imaging spectroscopy retrievals

The joint retrieval of surface reflectances and atmospheric parameters i...

0 Kelvin M. Leung, et al. ∙

research

∙ 07/23/2023

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices

We introduce a multifidelity estimator of covariance matrices formulated...

0 Aimee Maurais, et al. ∙

research

∙ 05/16/2023

A score-based operator Newton method for measure transport

Transportation of probability measures underlies many core tasks in stat...

0 Nisha Chandramoorthy, et al. ∙

research

∙ 05/10/2023

Principal Feature Detection via Φ-Sobolev Inequalities

We investigate the approximation of high-dimensional target measures as ...

0 Matthew T. C. Li, et al. ∙

research

∙ 04/01/2023

Diffusion map particle systems for generative modeling

We propose a novel diffusion map particle system (DMPS) for generative m...

0 Fengyi Li, et al. ∙

research

∙ 02/20/2023

Infinite-Dimensional Diffusion Models for Function Spaces

We define diffusion-based generative models in infinite dimensions, and ...

0 Jakiw Pidstrigach, et al. ∙

research

∙ 01/31/2023

Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry

We introduce a multi-fidelity estimator of covariance matrices that empl...

0 Aimee Maurais, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 07/20/2022

On minimax density estimation via measure transport

We study the convergence properties, in Hellinger and related distances,...

0 Sven Wang, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/01/2021

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of da...

0 Ki Tae Kim, et al. ∙

research

∙ 11/13/2021

Computing f-Divergences and Distances of High-Dimensional Probability Density Functions – Low-Rank Tensor Approximations

Very often, in the course of uncertainty quantification tasks or data an...

0 Alexander Litvinenko, et al. ∙

research

∙ 07/28/2021

Sparse approximation of triangular transports. Part II: the infinite dimensional case

For two probability measures ρ and π on [-1,1]^ℕ we investigate the appr...

0 Jakob Zech, et al. ∙

research

∙ 05/14/2021

Bayesian inference under model misspecification using transport-Lagrangian distances: an application to seismic inversion

Model misspecification constitutes a major obstacle to reliable inferenc...

0 Andrea Scarinci, et al. ∙

research

∙ 02/20/2021

Nonlinear dimension reduction for surrogate modeling using gradient information

We introduce a method for the nonlinear dimension reduction of a high-di...

0 Daniele Bigoni, et al. ∙

research

∙ 01/18/2021

A Koopman framework for rare event simulation in stochastic differential equations

We exploit the relationship between the stochastic Koopman operator and ...

0 Benjamin Zhang, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 06/12/2020

Sparse approximation of triangular transports on bounded domains

Let ρ and π be two probability measures on [-1,1]^d with positive and an...

0 Jakob Zech, et al. ∙

research

∙ 06/11/2020

Conditional Sampling With Monotone GANs

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

0 Nikola Kovachki, et al. ∙

research

∙ 06/03/2020

Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design

We propose and analyze batch greedy heuristics for cardinality constrain...

0 Jayanth Jagalur-Mohan, et al. ∙

research

∙ 05/29/2020

Rate-optimal refinement strategies for local approximation MCMC

Many Bayesian inference problems involve target distributions whose dens...

0 Andrew Davis, et al. ∙

research

∙ 04/25/2020

Low-rank multi-parametric covariance identification

We propose a differential geometric construction for families of low-ran...

0 Antoni Musolas, et al. ∙

research

∙ 03/18/2020

Data-Driven Forward Discretizations for Bayesian Inversion

This paper suggests a framework for the learning of discretizations of e...

0 Daniele Bigoni, et al. ∙

research

∙ 01/06/2020

Geodesically parameterized covariance estimation

Statistical modeling of spatiotemporal phenomena often requires selectin...

0 Antoni Musolas, et al. ∙

research

∙ 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. ∙

research

∙ 05/31/2019

Greedy inference with layers of lazy maps

We propose a framework for the greedy approximation of high-dimensional ...

0 Daniele Bigoni, et al. ∙

research

∙ 03/03/2019

Scalable optimization-based sampling on function space

Optimization-based samplers provide an efficient and parallellizable app...

0 Zheng Wang, et al. ∙

research

∙ 08/28/2018

A transport-based multifidelity preconditioner for Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) sampling of posterior distributions aris...

0 Benjamin Peherstorfer, et al. ∙

research

∙ 07/02/2018

Certified dimension reduction in nonlinear Bayesian inverse problems

We propose a dimension reduction technique for Bayesian inverse problems...

0 Olivier Zahm, et al. ∙

research

∙ 06/08/2018

A Stein variational Newton method

Stein variational gradient descent (SVGD) was recently proposed as a gen...

0 Gianluca Detommaso, et al. ∙

research

∙ 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. ∙

research

∙ 05/12/2017

Exploiting network topology for large-scale inference of nonlinear reaction models

The development of chemical reaction models aids system design and optim...

0 Nikhil Galagali, et al. ∙

research

∙ 03/17/2017

Inference via low-dimensional couplings

We investigate the low-dimensional structure of deterministic transforma...

0 Alessio Spantini, et al. ∙

Youssef Marzouk

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