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. Flock R, Dong Y, Uribe F, and Zahm O. Continuous Gaussian mixture solution for linear Bayesian inversion with application to Laplace priors. arXiv:2408.16594, 2024.
  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. Verdière R, Prieur C, Zahm O. Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space. hal-04364208. 2023 December 26.
  5. Cui T, Dolgov S, Zahm O. Self-reinforced polynomial approximation methods for concentrated probability densities. arXiv preprint arXiv:2303.02554. 2023 Mar 5.
  6. 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. Li M, Marzouk Y, Zahm O. Principal Feature Detection via Phi-Sobolev Inequalities. Bernoulli. Nov 2024. 30(4)2979–3003.
  2. 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).
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Cui T, Zahm O. Data-free likelihood-informed dimension reduction of Bayesian inverse problems. Inverse Problems. 2021 Mar 17;37(4):045009.
  10. 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.
  11. 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.
  12. 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.
  13. Zahm O, Constantine PG, Prieur C, Marzouk YM. Gradient-based dimension reduction of multivariate vector-valued functions. SIAM:SISC. 2020;42(1):A534-58.
  14. Lam RR, Zahm O, Marzouk YM, Willcox KE. Multifidelity dimension reduction via active subspaces. SIAM:SISC. 2020;42(2):A929-56.
  15. Smetana K, Zahm O, Patera AT. Randomized residual-based error estimators for parametrized equations. SIAM:SISC. 2019;41(2):A900-26.
  16. 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.
  17. 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.
  18. Zahm O, Nouy A. Interpolation of inverse operators for preconditioning parameter-dependent equations. SIAM:SISC. 2016;38(2):A1044-74.
  19. 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.
  20. 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.

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