Youssef M. Marzouk

Orcid: 0000-0001-8242-3290

Affiliations:
  • Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA USA (PhD)


According to our database1, Youssef M. Marzouk authored at least 74 papers between 2005 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities.
CoRR, 2024

Stable generative modeling using diffusion maps.
CoRR, 2024

Sampling in Unit Time with Kernel Fisher-Rao Flow.
CoRR, 2024

2023
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty.
ACM Trans. Math. Softw., June, 2023

Computing f -divergences and distances of high-dimensional probability density functions.
Numer. Linear Algebra Appl., May, 2023

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference.
CoRR, 2023

Distribution learning via neural differential equations: a nonparametric statistical perspective.
CoRR, 2023

A transport approach to sequential simulation-based inference.
CoRR, 2023

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices.
CoRR, 2023

A score-based operator Newton method for measure transport.
CoRR, 2023

Diffusion map particle systems for generative modeling.
CoRR, 2023

An Approximation Theory Framework for Measure-Transport Sampling Algorithms.
CoRR, 2023

Infinite-Dimensional Diffusion Models for Function Spaces.
CoRR, 2023

Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry.
Proceedings of the International Conference on Machine Learning, 2023

2022
MParT: Monotone Parameterization Toolkit.
J. Open Source Softw., December, 2022

Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics.
Stat. Comput., 2022

Rate-optimal refinement strategies for local approximation MCMC.
Stat. Comput., 2022

Certified dimension reduction in nonlinear Bayesian inverse problems.
Math. Comput., 2022

A Koopman framework for rare event simulation in stochastic differential equations.
J. Comput. Phys., 2022

2021
Geodesically Parameterized Covariance Estimation.
SIAM J. Matrix Anal. Appl., 2021

Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation.
SIAM/ASA J. Uncertain. Quantification, 2021

Batch greedy maximization of non-submodular functions: Guarantees and applications to experimental design.
J. Mach. Learn. Res., 2021

Computing f-Divergences and Distances of High-Dimensional Probability Density Functions - Low-Rank Tensor Approximations.
CoRR, 2021

Computing eigenfunctions of the multidimensional Ornstein-Uhlenbeck operator.
CoRR, 2021

Sparse approximation of triangular transports. Part II: the infinite dimensional case.
CoRR, 2021

Nonlinear dimension reduction for surrogate modeling using gradient information.
CoRR, 2021

Learning non-Gaussian graphical models via Hessian scores and triangular transport.
CoRR, 2021

2020
Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions.
SIAM J. Sci. Comput., 2020

MALA-within-Gibbs Samplers for High-Dimensional Distributions with Sparse Conditional Structure.
SIAM J. Sci. Comput., 2020

Multifidelity Dimension Reduction via Active Subspaces.
SIAM J. Sci. Comput., 2020

Scalable Optimization-Based Sampling on Function Space.
SIAM J. Sci. Comput., 2020

An adaptive transport framework for joint and conditional density estimation.
CoRR, 2020

Sparse approximation of triangular transports on bounded domains.
CoRR, 2020

Conditional Sampling With Monotone GANs.
CoRR, 2020

Data-Driven Forward Discretizations for Bayesian Inversion.
CoRR, 2020

Greedy inference with structure-exploiting lazy maps.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Localization for MCMC: sampling high-dimensional posterior distributions with local structure.
J. Comput. Phys., 2019

A transport-based multifidelity preconditioner for Markov chain Monte Carlo.
Adv. Comput. Math., 2019

2018
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection.
Pattern Recognit., 2018

Transport Map Accelerated Markov Chain Monte Carlo.
SIAM/ASA J. Uncertain. Quantification, 2018

Parallel Local Approximation MCMC for Expensive Models.
SIAM/ASA J. Uncertain. Quantification, 2018

Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals.
SIAM/ASA J. Uncertain. Quantification, 2018

Inference via Low-Dimensional Couplings.
J. Mach. Learn. Res., 2018

High-dimensional stochastic optimal control using continuous tensor decompositions.
Int. J. Robotics Res., 2018

A Stein variational Newton method.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Bayesian Inverse Problems with l<sub>1</sub> Priors: A Randomize-Then-Optimize Approach.
SIAM J. Sci. Comput., 2017

Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems.
SIAM J. Sci. Comput., 2017

Exploiting network topology for large-scale inference of nonlinear reaction models.
CoRR, 2017

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Piecewise-Bézier C1 smoothing on manifolds with application to wind field estimation.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016
Spectral Tensor-Train Decomposition.
SIAM J. Sci. Comput., 2016

A Multiscale Strategy for Bayesian Inference Using Transport Maps.
SIAM/ASA J. Uncertain. Quantification, 2016

Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation.
SIAM/ASA J. Uncertain. Quantification, 2016

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction.
J. Comput. Phys., 2016

Dimension-independent likelihood-informed MCMC.
J. Comput. Phys., 2016

Automated synthesis of low-rank control systems from sc-LTL specifications using tensor-train decompositions.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015
Optimal Low-rank Approximations of Bayesian Linear Inverse Problems.
SIAM J. Sci. Comput., 2015

Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition.
Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015

2014
Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems.
SIAM J. Sci. Comput., 2014

Efficient Localization of Discontinuities in Complex Computational Simulations.
SIAM J. Sci. Comput., 2014

A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework.
Proceedings of the Dynamic Data-Driven Environmental Systems Science, 2014

2013
Adaptive Smolyak Pseudospectral Approximations.
SIAM J. Sci. Comput., 2013

Simulation-based optimal Bayesian experimental design for nonlinear systems.
J. Comput. Phys., 2013

2012
Sequential data assimilation with multiple models.
J. Comput. Phys., 2012

Bayesian inference with optimal maps.
J. Comput. Phys., 2012

Data-free inference of the joint distribution of uncertain model parameters.
J. Comput. Phys., 2012

Texton-based segmentation and classification of human embryonic stem cell colonies using multi-stage Bayesian level sets.
Proceedings of the 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2012

2011
Bayesian Inference of Atomic Diffusivity in a Binary Ni/Al System Based on Molecular Dynamics.
Multiscale Model. Simul., 2011

A unified approach to expectation-maximization and level set segmentation applied to stem cell and brain MRI images.
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011

2009
Convergence Characteristics and Computational Cost of Two Algebraic Kernels in Vortex Methods with a Tree-Code Algorithm.
SIAM J. Sci. Comput., 2009

Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems.
J. Comput. Phys., 2009

2007
Stochastic spectral methods for efficient Bayesian solution of inverse problems.
J. Comput. Phys., 2007

2005
Meaningful Automated Statistical Analysis of Large Computational Clusters.
Proceedings of the 2005 IEEE International Conference on Cluster Computing (CLUSTER 2005), September 26, 2005


  Loading...