41 citations · 198 across the 24 of their papers we have counts for
4 papers · 1 filter
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reduction
Tiangang Cui, Sergey Dolgov, Olivier Zahm
We present a novel offline-online method to mitigate the computational burden of the characterization of posterior random variables in statistical learning. In the offline phase, t…
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems
Tiangang Cui, Olivier Zahm
Identifying a low-dimensional informed parameter subspace offers a viable path to alleviating the dimensionality challenge in the sampled-based solution to large-scale Bayesian inv…
Nonlinear dimension reduction for surrogate modeling using gradient information
Daniele Bigoni, Youssef Marzouk, Clémentine Prieur +1
We introduce a method for the nonlinear dimension reduction of a high-dimensional function , . Our objective is to identify a nonlinear…
Learning non-Gaussian graphical models via Hessian scores and triangular transport
Ricardo Baptista, Youssef Marzouk, Rebecca E. Morrison +1
Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables. Knowing the sparsity of such a graphica…