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Youssef Marzouk

Learning Probabilistic Graphical Models of Non-Gaussian Scientific Data
Ensemble transport smoothing--Part 1: unified framework
Ensemble transport smoothing--Part 2: nonlinear updates
Gradient-based dimension reduction for solving Bayesian inverse problems
Towards high-dimensional sequential inference
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
On the representation and learning of monotone triangular transport maps
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
Representation and optimization of triangular transports
A low-rank ensemble Kalman filter for elliptic observations
Low-Dimensional Structure in Bayesian Inference Problems with Mixture Models
Sequential Bayesian inference via structured nonlinear couplings
Ensemble-based data assimilation via nonlinear couplings
Likelihood-free Bayesian inference via couplings
Learning non-Gaussian graphical models via Hessian scores and triangular transport
A low-rank nonlinear ensemble filter for vortex models of aerodynamic flows
Learning non-Gaussian graphical models
Conditional Sampling With Monotone GANs
High-dimensional ensemble filtering with nonlinear local couplings
Coupling techniques for nonlinear ensemble filtering
Optimal approximations of coupling in multidisciplinary models
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting