Optimal Low-rank Approximations of Bayesian Linear Inverse Problems. - Alessio Spantini; Antti Solonen; Tiangang Cui; James Martin; Luis Tenorio; Youssef M. Marzouk (SIAM J. Sci. Comput., 2015)

Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems. - Alessio Spantini; Tiangang Cui; Karen Willcox; Luis Tenorio; Youssef M. Marzouk (SIAM J. Sci. Comput., 2017)

A Stein variational Newton method. - Gianluca Detommaso; Tiangang Cui; Youssef M. Marzouk; Alessio Spantini; Robert Scheichl (NeurIPS, 2018)

Inference via Low-Dimensional Couplings. - Alessio Spantini; Daniele Bigoni; Youssef M. Marzouk (J. Mach. Learn. Res., 2018)

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

Computing f-Divergences and Distances of High-Dimensional Probability Density Functions - Low-Rank Tensor Approximations. - Alexander Litvinenko; Youssef M. Marzouk; Hermann G. Matthies; Marco Scavino; Alessio Spantini (CoRR, 2021)

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

Computing f -divergences and distances of high-dimensional probability density functions. - Alexander Litvinenko; Youssef M. Marzouk; Hermann G. Matthies; Marco Scavino; Alessio Spantini (Numer. Linear Algebra Appl., 2023)