Interpolation of Inverse Operators for Preconditioning Parameter-Dependent Equations. - Olivier Zahm; Anthony Nouy (SIAM J. Sci. Comput., 2016)

Projection-Based Model Order Reduction Methods for the Estimation of Vector-Valued Variables of Interest. - Olivier Zahm; Marie Billaud Friess; Anthony Nouy (SIAM J. Sci. Comput., 2017)

Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems. - Kathrin Smetana; Olivier Zahm (CoRR, 2019)

Randomized Residual-Based Error Estimators for Parametrized Equations. - Kathrin Smetana; Olivier Zahm; Anthony T. Patera (SIAM J. Sci. Comput., 2019)

Greedy inference with structure-exploiting lazy maps. - Michael Brennan; Daniele Bigoni; Olivier Zahm; Alessio Spantini; Youssef M. Marzouk (NeurIPS, 2020)

An adaptive transport framework for joint and conditional density estimation. - Ricardo Baptista; Olivier Zahm; Youssef M. Marzouk (CoRR, 2020)

Multifidelity Dimension Reduction via Active Subspaces. - Rémi R. Lam; Olivier Zahm; Youssef M. Marzouk; Karen E. Willcox (SIAM J. Sci. Comput., 2020)

Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions. - Olivier Zahm; Paul G. Constantine; Clémentine Prieur; Youssef M. Marzouk (SIAM J. Sci. Comput., 2020)

Nonlinear dimension reduction for surrogate modeling using gradient information. - Daniele Bigoni; Youssef M. Marzouk; Clémentine Prieur; Olivier Zahm (CoRR, 2021)

Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems. - Tiangang Cui; Olivier Zahm (CoRR, 2021)

Conditional Deep Inverse Rosenblatt Transports. - Tiangang Cui; Sergey Dolgov; Olivier Zahm (CoRR, 2021)

Minimizing Rational Functions: A Hierarchy of Approximations via Pushforward Measures. - Jean-Bernard Lasserre; Victor Magron; Swann Marx; Olivier Zahm (SIAM J. Optim., 2021)

Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems. - Tiangang Cui; Xin Tong; Olivier Zahm (CoRR, 2022)

Certified dimension reduction in nonlinear Bayesian inverse problems. - Olivier Zahm; Tiangang Cui; Kody J. H. Law; Alessio Spantini; Youssef M. Marzouk (Math. Comput., 2022)

Self-reinforced polynomial approximation methods for concentrated probability densities. - Tiangang Cui; Sergey Dolgov; Olivier Zahm (CoRR, 2023)

Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. - Tiangang Cui; Sergey Dolgov; Olivier Zahm (J. Comput. Phys., 2023)

Sequential transport maps using SoS density estimation and α-divergences. - Benjamin Zanger; Tiangang Cui; Martin Schreiber 001; Olivier Zahm (CoRR, 2024)

Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities. - Matthew T. C. Li; Tiangang Cui; Fengyi Li; Youssef M. Marzouk; Olivier Zahm (CoRR, 2024)

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis. - Qiao Chen; Elise Arnaud; Ricardo Baptista; Olivier Zahm (CoRR, 2024)

Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport. - Ricardo Baptista; Rebecca E. Morrison; Olivier Zahm; Youssef M. Marzouk (J. Mach. Learn. Res., 2024)

Certified coordinate selection for high-dimensional Bayesian inversion with Laplace prior. - Rafael Flock; Yiqiu Dong; Felipe Uribe; Olivier Zahm (Stat. Comput., 2024)

On the Representation and Learning of Monotone Triangular Transport Maps. - Ricardo Baptista; Youssef M. Marzouk; Olivier Zahm (Found. Comput. Math., 2024)