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Olivier Zahm

Gradient-based dimension reduction for solving Bayesian inverse problems
Learning Probabilistic Graphical Models of Non-Gaussian Scientific Data
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
Learning non-Gaussian graphical models via Hessian scores and triangular transport
Learning non-Gaussian graphical models