Follow
Sébastien Marmin
Sébastien Marmin
Docteur en statistique, Laboratoire national de métrologie et d'essais
Verified email at lne.fr - Homepage
Title
Cited by
Cited by
Year
Differentiating the multipoint expected improvement for optimal batch design
S Marmin, C Chevalier, D Ginsbourger
International workshop on machine learning, optimization and big data, 37-48, 2015
602015
Warped gaussian processes and derivative-based sequential designs for functions with heterogeneous variations
S Marmin, D Ginsbourger, J Baccou, J Liandrat
SIAM/ASA Journal on Uncertainty Quantification 6 (3), 991-1018, 2018
262018
Kernel computations from large-scale random features obtained by optical processing units
R Ohana, J Wacker, J Dong, S Marmin, F Krzakala, M Filippone, L Daudet
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
192020
Efficient batch-sequential bayesian optimization with moments of truncated gaussian vectors
S Marmin, C Chevalier, D Ginsbourger
arXiv preprint arXiv:1609.02700, 2016
132016
DiceOptim: Kriging-based optimization for computer experiments
V Picheny, D Ginsbourger, O Roustant, M Binois, S Marmin, T Wagner
R package version 0.8-1, 2016
102016
Walsh-hadamard variational inference for Bayesian deep learning
S Rossi, S Marmin, M Filippone
Advances in Neural Information Processing Systems 33, 9674-9686, 2020
92020
Variational calibration of computer models
S Marmin, M Filippone
arXiv preprint arXiv:1810.12177, 2018
82018
Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
R Ohana, J Wacker, J Dong, S Marmin, F Krzakala, M Filippone, L Daudet
IEEE, 2020
62020
Deep Gaussian Processes for Calibration of Computer Models
S Marmin, M Filippone
Bayesian Analysis 1 (1), 1-30, 2022
32022
Warping and sampling approaches to non-stationary gaussian process modelling.
S Marmin
Ecole centrale de Marseille, 2017
12017
Planification adaptative d'expériences numériques par paquets en contexte non stationnaire pour une étude de fissuration mécanique
S Marmin, J Baccou, F Péralès, D Ginsbourger, J Liandrat
CFM 2017-23ème Congrès Français de Mécanique, 2017
12017
Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling
S Marmin, J Baccou, J Liandrat, D Ginsbourger
Wavelets and Sparsity XVII 10394, 413-422, 2017
12017
Processus gaussiens déformés pour l'apprentissage de zones instationnaires
S Marmin, D Ginsbourger, J Baccou, F Perales, J Liandrat
47èmes Journées de Statistique de la SFdS, 2015
12015
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
S Rossi, S Marmin, M Filippone
arXiv preprint arXiv:1912.00015, 2019
2019
Adaptive design experiment and non-stationary kriging: applications to uncertainty analysis in nuclear mechanical studies
S Marmin, D Ginsbourger, J Baccou, J Liandrat, F Perales
Friction, Fracture, Failure [Microstructural Effects], 2015
2015
Adaptive design for the estimation of high-variation regions using non-stationary Gaussian process models
S Marmin, D Ginsbourger, J Baccou, J Liandrat, F Perales
Eighth International Workshop on Simulation, 2015
2015
Non-stationary Gaussian process modelling and sequential design of experiments for exploration of high variation regions
S Marmin, D Ginsbourger, J Baccou, J Liandrat
Supplement material for “Walsh-Hadamard Variational Inference for Bayesian Deep Learning”
S Rossi, S Marmin, M Filippone
Gradient of the multipoint expected improvement (qEI) criterion
S Marmin, C Chevalier, D Ginsbourger
eps 10, 6, 0
Sequential multipoint Expected improvement (qEI) maximizations and model re-estimation
S Marmin, C Chevalier, D Ginsbourger
The system can't perform the operation now. Try again later.
Articles 1–20