Chair of applied mathematics OQUAIDO

Optimization and uncertainty quantification for expensive data

Cross classification scientific production
Inversion /
Categorical inputs S1 S4 P7
Stochastic codes
Functional inputs/outputs
High number of inputs
P6 P5 J2 J5
Specific constraints S2 P1 P4
High number of data S3 J3 J4 P2
Other topics J1 J1 S1 P3 C1

Software(*) (S)

  1. kergp: Kernel laboratory. This package, created during the DICE consortium, has been enriched with new functionalities: categorical variables, radial kernels, optimizer choices, etc.
  2. lineqGPR : Gaussian Process Regression Models with Linear Inequality Constraints.
  3. nestedKriging : Nested kriging models for large data sets.
  4. mixgp: Kriging models with both discrete and continuous input variables. Will be included in kergp.

Publications in journals (J)

  1. Universal Prediction Distribution for Surrogate Models, M. Ben Salem, O. Roustant, F. Gamboa, and L. Tomaso (2017), SIAM/ASA Journal on Uncertainty Quantification, 5 (1), 1086-1109.
  2. Poincaré inequalities on intervals - application to sensitivity analysis O. Roustant, F. Barthe and B. Iooss (2017), Electronic Journal of Statistics, 11 (2), 3081-3119.
  3. Variational Fourier Features for Gaussian Processes J. Hensman, N. Durrande and A. Solin (2018), Journal of Machine Learning Research, 8, 1-52.
  4. Nested Kriging predictions for datasets with a large number of observations, D. Rullière, N. Durrande, F. Bachoc and C. Chevalier (2018), Statistics and Computing, 28 (4), 849-867.
  5. Sensitivity Analysis Based on Cramér von Mises Distance, F. Gamboa, T. Klein, and A. Lagnoux (2018), SIAM/ASA Journal on Uncertainty Quantification, 6 (2), 522-548.

Preprints (P)

  1. Finite-dimensional Gaussian approximation with linear inequality constraints, A.F. López-Lopera, F. Bachoc, N. Durrande and O. Roustant (2017).
  2. Some properties of nested Kriging predictors, F. Bachoc, N. Durrande, D. Rullière and C. Chevalier (2017).
  3. Karhunen-Loève decomposition of Gaussian measures on Banach spaces, X. Bay and J.C. Croix (2017).
  4. Maximum likelihood estimation for Gaussian processes under inequality constraints, F. Bachoc, A. Lagnoux and A.F. López-Lopera (2018).
  5. Data-driven stochastic inversion under functional uncertainties, M.R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2018).
  6. Sequential dimension reduction for learning features of expensive black-box functions, M. Ben Salem, F. Bachoc, O. Roustant, F. Gamboa F and L. Tomaso (2018).
  7. Group kernels for Gaussian process metamodels with categorical inputs, O. Roustant, E. Padonou, Y. Deville, A. Clément, G. Perrin, J. Giorla and H. Wynn (2018).

Conference proceedings (C)

  1. Gaussian Processes For Computer Experiments, F. Bachoc, E. Contal, H. Maatouk, and D. Rullière (2017), ESAIM: Proceedings and surveys, proceedings of MAS2016 conference, 60, p. 163-179.

(*) One of the Chair activities is to develop opensource R packages that are later available on the CRAN archive website.