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 | 67 | 2015 |
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 | 31 | 2018 |
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 | 27 | 2020 |
Walsh-hadamard variational inference for bayesian deep learning S Rossi, S Marmin, M Filippone Advances in Neural Information Processing Systems 33, 9674-9686, 2020 | 16 | 2020 |
Deep gaussian processes for calibration of computer models (with discussion) S Marmin, M Filippone Bayesian Analysis 17 (4), 1301-1350, 2022 | 14 | 2022 |
DiceOptim: Kriging-based optimization for computer experiments V Picheny, D Ginsbourger, O Roustant, M Binois, S Marmin, T Wagner R package version 2, 2016 | 14 | 2016 |
Efficient batch-sequential bayesian optimization with moments of truncated gaussian vectors S Marmin, C Chevalier, D Ginsbourger arXiv preprint arXiv:1609.02700, 2016 | 12 | 2016 |
Variational calibration of computer models S Marmin, M Filippone arXiv preprint arXiv:1810.12177, 2018 | 8 | 2018 |
Warping and sampling approaches to non-stationary gaussian process modelling. S Marmin Ecole centrale de Marseille, 2017 | 4 | 2017 |
Developpements pour l’evaluation et la maximisation du critere d’amelioration esperee multipoint en optimisation globale S Marmin Master’s thesis, Ecole nationale supérieure des Mines de Saint-Etienne, 2014 | 4 | 2014 |
Input uncertainty propagation through trained neural networks P Monchot, L Coquelin, SJ Petit, S Marmin, E Le Pennec, N Fischer International Conference on Machine Learning 2023, 2023 | 1 | 2023 |
Efficient approximate inference with Walsh-Hadamard variational inference S Rossi, S Marmin, M Filippone arXiv preprint arXiv:1912.00015, 2019 | 1 | 2019 |
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 | 1 | 2017 |
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 | 1 | 2017 |
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 | 1 | 2015 |
The strategic research agenda of the European Metrology Network Mathmet S Heidenreich, M Bär, C Elster, O Henze, K Lines, S Rhodes, M Cox, ... ENBIS and EMN Mathmet Joint Workshop Mathematical and Statistical Methods …, 2023 | | 2023 |
Mathmet Quality Assurance Tools for data, software, and guidelines K Lines, JL Hippolyte, I George, P Harris, N Fischer, D Gumuchian, ... ENBIS and EMN Mathmet Joint Workshop Mathematical and Statistical Methods …, 2023 | | 2023 |
Physiological variability in brain electric conductivity: correcting the effect of the age for the detection of pathological alterations S Marmin, A Arduino, M Cencini, M Lancione, L Biagi, M Tosetti, L Zilberti Joint workshop of ENBIS and MATHMET Mathematical and Statistical Methods for …, 2023 | | 2023 |
Metrological characterisation of quantitative MRI techniques in completely controlled conditions S Marmin, A Arduino, A Bosnjakovic, L Zilberti Book of abstracts, 2022 | | 2022 |
A MATHMET Quality Management System for data and software K Lines, JL Hippolyte, P Harris, N Fischer, S Marmin, S Ellison, S Cowen, ... Joint Workshop of ENBIS and MATHMET" Mathematical and Statistical Methods …, 2021 | | 2021 |