Youssef M. Marzouk
Orcid: 0000-0001-8242-3290Affiliations:
- Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA USA (PhD)
According to our database1,
Youssef M. Marzouk
authored at least 84 papers
between 2005 and 2024.
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Bibliography
2024
Evaluating the Accuracy of Gaussian Approximations in VSWIR Imaging Spectroscopy Retrievals.
IEEE Trans. Geosci. Remote. Sens., 2024
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference.
SIAM/ASA J. Uncertain. Quantification, 2024
Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective.
J. Mach. Learn. Res., 2024
J. Mach. Learn. Res., 2024
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport.
J. Comput. Phys., 2024
CoRR, 2024
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities.
CoRR, 2024
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities.
CoRR, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
J. Comput. Phys. X, November, 2023
J. Comput. Phys. X, November, 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
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices.
CoRR, 2023
CoRR, 2023
Proceedings of the International Conference on Machine Learning, 2023
2022
Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics.
Stat. Comput., 2022
Math. Comput., 2022
J. Comput. Phys., 2022
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
CoRR, 2021
Sparse approximation of triangular transports. Part II: the infinite dimensional case.
CoRR, 2021
CoRR, 2021
2020
SIAM J. Sci. Comput., 2020
MALA-within-Gibbs Samplers for High-Dimensional Distributions with Sparse Conditional Structure.
SIAM J. Sci. Comput., 2020
CoRR, 2020
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
Adv. Comput. Math., 2019
2018
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection.
Pattern Recognit., 2018
SIAM/ASA J. Uncertain. Quantification, 2018
SIAM/ASA J. Uncertain. Quantification, 2018
Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals.
SIAM/ASA J. Uncertain. Quantification, 2018
Int. J. Robotics Res., 2018
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
SIAM J. Sci. Comput., 2017
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
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
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
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
SIAM J. Sci. Comput., 2014
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
J. Comput. Phys., 2013
2012
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
J. Comput. Phys., 2007
2005
Proceedings of the 2005 IEEE International Conference on Cluster Computing (CLUSTER 2005), September 26, 2005