Cross classification scientific production
High number of inputs
High number of data
kergp: Kernel laboratory. This package, created during the ReDICE consortium, has been enriched with new functionalities:
categorical variables, radial kernels, optimizer choices, etc.
lineqGPR : Gaussian Process Regression Models with Linear Inequality Constraints.
nestedKriging : Nested kriging models for large data sets.
mixgp: Kriging models with both discrete and continuous input variables. Will be included in kergp.
Publications in journals (J)
M. Ben Salem, O. Roustant, F. Gamboa, and L. Tomaso (2017), Universal Prediction Distribution for Surrogate Models SIAM/ASA Journal on Uncertainty Quantification, 5 (1), 1086-1109.
Poincaré inequalities on intervals - application to sensitivity analysis
O. Roustant, F. Barthe and B. Iooss (2017),
Electronic Journal of Statistics, 11 (2), 3081-3119.
Variational Fourier Features for Gaussian Processes
J. Hensman, N. Durrande and A. Solin (2018),
Journal of Machine Learning Research, 8, 1-52.
D. Rullière, N. Durrande, F. Bachoc and C. Chevalier (2018), Nested Kriging predictions for datasets with a large number of observations Statistics and Computing, 28 (4), 849-867.
F. Gamboa, T. Klein, and A. Lagnoux (2018), SIAM/ASA Journal on Uncertainty Quantification, Sensitivity Analysis Based on Cramér von Mises Distance 6 (2), 522-548.
A.F. López-Lopera, F. Bachoc, N. Durrande and O. Roustant (2018), SIAM/ASA J. Uncertainty Quantification, Finite-dimensional Gaussian approximation with linear inequality constraints 6 (3), 1224–1255.
M.R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2019), Data-driven stochastic inversion via functional quantization Statistics and Computing, online publication on Sept. 13. ↵
F. Bachoc, A. Lagnoux and A.F. López-Lopera (2019), Electronic Journal of Statistics, Maximum likelihood estimation for Gaussian processes under inequality constraints 13 (2), 2921--2969. ↵
D. Azzimonti, D. Ginsbourger, J. Rohmer and D. Idier (2019), Technometrics, online version in January.
Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. ↵
M. Ben Salem, F. Bachoc, O. Roustant, F. Gamboa F and L. Tomaso (2019), to appear in SIAM/ASA Journal on Uncertainty Quantification. Sequential dimension reduction for learning features of expensive black-box functions ↵
X. Bay and J.C. Croix (2019), to appear in Probability and Mathematical Statistics. Karhunen-Loève decomposition of Gaussian measures on Banach spaces ↵
F. Bachoc, N. Durrande, D. Rullière and C. Chevalier (2017). Some properties of nested Kriging predictors
O. Roustant, E. Padonou, Y. Deville, A. Clément, G. Perrin, J. Giorla and H. Wynn (2018). Group kernels for Gaussian process metamodels with categorical inputs
Conference proceedings (C)
F. Bachoc, E. Contal, H. Maatouk, and D. Rullière (2017), Gaussian Processes For Computer Experiments ESAIM: Proceedings and surveys, proceedings of MAS2016 conference, 60, p. 163-179.
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints,
A. F. López-Lopera, S. John, and N. Durrande (2019), PMLR:, proceedings of AISTATS19 conference, 89, p. 1997-2006.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC,
A. F. López-Lopera, F. Bachoc, N. Durrande, J. Rohmer, D. Idier, and O. Roustant (2019), to appear in Monte Carlo and Quasi-Monte Carlo Methods:, proceedings of MCQMC18 conference, 13, p. 355-371.
One of the Chair activities is to develop opensource R packages
that are later available on the CRAN archive website.