Olivier Zahm

Research scientist at Inria, Laboratoire Jean Kuntzmann.

  • Email: olivier.zahm [at] inria [dot] fr
  • Phone: +33 4 57 42 17 70
  • Address: Bâtiment IMAG, 150 place du Torrent, 38400 Saint Martin d’Hères.

Preprints

  1. Verdière R, Prieur C, and Zahm O. Mollified Active Subspace. hal-05194926, August 2025.
  2. Cui T, Tong X, Zahm O. Optimal Riemannian metric for Poincaré inequalities and how to ideally precondition Langevin dymanics. arXiv:2404.02554, 2024 April.
  3. Zanger B, Cui T, Schreiber M, Zahm O. Sequential transport maps using SoS density estimation and alpha-divergences. arXiv:2402.17943, 2024 March.
  4. Cui T, Dolgov S, Zahm O. Self-reinforced polynomial approximation methods for concentrated probability densities. arXiv preprint arXiv:2303.02554. 2023 Mar 5.
  5. Baptista R, Marzouk Y, Zahm O. Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective. arXiv preprint arXiv:2207.08670. 2022 Jul 18.

Publications

  1. Verdière R, Prieur C, Zahm O. Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space. Journal of Machine Learning Research. 2025 ; 26(139):1-31.
  2. Li MT, Cui T, Li F, Marzouk Y, Zahm O. Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities. Information and Inference: A Journal of the IMA. 2025 Sep; 14(3):iaaf021.
  3. Chen Q, Arnaud E, Baptista R, and Zahm O. Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis. SIAM:SISC. 2025; 47(5):A2403-A2430.
  4. Flock R, Dong Y, Uribe F, and Zahm O. Continuous Gaussian mixture solution for linear Bayesian inversion with application to Laplace priors. Inverse Problems. 2025 ; 41(6):065012.
  5. Li M, Marzouk Y, Zahm O. Principal Feature Detection via Phi-Sobolev Inequalities. Bernoulli. Nov 2024. 30(4)2979–3003.
  6. Flock R, Dong Y, Uribe F, Zahm O. Certified coordinate selection for high-dimensional Bayesian inversion with Laplace prior. Statistics and Computing. 2024, 34(134).
  7. Baptista R, Marzouk Y, Morrison RE, Zahm O. Learning non-Gaussian graphical models via Hessian scores and triangular transport. Journal of Machine Learning Research. 2024 Apr 11. 25(85):1–46.
  8. Baptista R, Marzouk Y, Zahm O. On the representation and learning of monotone triangular transport maps. Foundations of Computational Mathematics. 2023 Nov 16. 1615-3383.
  9. Cui T, Dolgov S, Zahm O. Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. Journal of Computational Physics. 2023 Jul 15;485:112103.
  10. Cui T, Tong XT, Zahm O. Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems. Inverse Problems. 2022 Oct 19;38(12):124002.
  11. Bigoni D, Marzouk Y, Prieur C, Zahm O. Nonlinear dimension reduction for surrogate modeling using gradient information. Information and Inference: A Journal of the IMA. 2022 Dec;11(4):1597-639.
  12. Zahm O, Cui T, Law K, Spantini A, Marzouk Y. Certified dimension reduction in nonlinear Bayesian inverse problems. Mathematics of Computation. 2022 Jul;91(336):1789-835.
  13. Cui T, Zahm O. Data-free likelihood-informed dimension reduction of Bayesian inverse problems. Inverse Problems. 2021 Mar 17;37(4):045009.
  14. Lasserre JB, Magron V, Marx S, Zahm O. Minimizing rational functions: a hierarchy of approximations via pushforward measures. SIAM Journal on Optimization. 2021;31(3):2285-306.
  15. Brennan M, Bigoni D, Zahm O, Spantini A, Marzouk Y. Greedy inference with structure-exploiting lazy maps. Advances in Neural Information Processing Systems. 2020;33:8330-42.
  16. Smetana K, Zahm O. Randomized residual‐based error estimators for the proper generalized decomposition approximation of parametrized problems. International journal for numerical methods in engineering. 2020 Dec 15;121(23):5153-77.
  17. Zahm O, Constantine PG, Prieur C, Marzouk YM. Gradient-based dimension reduction of multivariate vector-valued functions. SIAM:SISC. 2020;42(1):A534-58.
  18. Lam RR, Zahm O, Marzouk YM, Willcox KE. Multifidelity dimension reduction via active subspaces. SIAM:SISC. 2020;42(2):A929-56.
  19. Smetana K, Zahm O, Patera AT. Randomized residual-based error estimators for parametrized equations. SIAM:SISC. 2019;41(2):A900-26.
  20. Ji W, Wang J, Zahm O, Marzouk YM, Yang B, Ren Z, Law CK. Shared low-dimensional subspaces for propagating kinetic uncertainty to multiple outputs. Combustion and Flame. 2018 Apr 1;190:146-57.
  21. Zahm O, Billaud-Friess M, Nouy A. Projection-based model order reduction methods for the estimation of vector-valued variables of interest. SIAM:SISC. 2017;39(4):A1647-74.
  22. Zahm O, Nouy A. Interpolation of inverse operators for preconditioning parameter-dependent equations. SIAM:SISC. 2016;38(2):A1044-74.
  23. Cazeaux P, Zahm O. A fast boundary element method for the solution of periodic many-inclusion problems via hierarchical matrix techniques. ESAIM: Proceedings and Surveys. 2015 Jan 1;48:156-68.
  24. Billaud-Friess M, Nouy A, Zahm O. A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems. ESAIM:M2AN. 2014 Nov;48(6):1777-806.

PhD thesis (supervisor: Anthony Nouy and Marie Billaud-Freiss):

Model order reduction methods for parameter-dependent equations: Applications in Uncertainty Quantification, 2015. link.