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Benchmarking XAI Explanations with Human-Aligned Evaluations
R. Kazmierczak, S. Azzolin, E. Berthier, A. Hedstrom, P. Delhomme, N. Bousquet, G. Frehse, M. Mancini, B. Caramiaux, A. Passerini, G. Franchi
arXiv:2411.02470 (2024)

Contribution of Subsets of Variables in Global Sensitivity Analysis with Dependent Variables
C. Labreuche
Proceedings of Scalable Uncertainty Management (SUM) Conference , (2024)

Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid
N. Atienza, R. Bresson, C. Rousselot, P. Caillou, J. Cohen, C. Labreuche, M. Sebag
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24) , 3669-3678, Jeju, South Corea (2024)

Dynamic reservoir prediction and well control optimization using physics-informed framework
Z. Elabid, D. Busby, A. Hadid, C. Targe
Proceedings of the European Conference on the Mathematics of Geological Reservoirs (ECMOR) , Oslo, Norway (2024)

A Graph Neural Network-Based Approach for Complex Reservoirs Simulation Surrogate Modelling
L. Sasal, D. Busby, A. Hadid
Proceedings of the European Conference on the Mathematics of Geological Reservoirs (ECMOR) , Oslo, Norway (2024)

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
E. Jaber, V. Blot, N. Brunel, V. Chabridon, E. Remy, B. Iooss, D. Lucor, M. Mougeot, A. Leite
Journal of Machine Learning for Modeling and Computing (2025) (in press)

A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
A. Ribes, N. Benchekroun, T. Delagnes
Proceedings of AI4Science@ICML Workshop (2024)

Computing conservative probabilities of rare events using surrogates
N. Bousquet
arXiv:2403.17505 (2024)

Hoeffding's decomposition of functions of random dependent variables
M. Il Idrissi, N. Bousquet, F. Gamboa, B. Iooss, J.-M. Loubes
Journal of Multivariate Analysis, 208: 105444 (2025)

Towards simulation of radio-frequency component with physics informed neural networks
M. Riou, M. Coulibaly, J.-P. Marcy
Proceedings of the 2023 Conference on Artificial Intelligence for Defense (CAID'23), HAL-04328433 (2023)

Un cadre méthodologique pour assurer la spécification d'un simulateur par apprentissage machine profond en vue de sa validation
C. Denis
HAL-04277064 (2023)

Covariance constraints for stochastic inverse problems of computer models
N. Bousquet, M. Blazère, T. Cerbelaud
Electronic Journal of Statistics 19: 1809-1854 (2025)

Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
M. Il Idrissi, N. Bousquet, F. Gamboa, B. Iooss, J.-M. Loubes
Electronic Journal of Statistics , 18:2721-2770 (2024)

Associated gitlab

Neural network surrogates for atmospheric dispersion in built area
A. de Villeroch, R.-S. Mouradi, V. Le Guen, P. Massin, A. Farchi, M. Bocquet, P. Armand
Proceedings of HARMO22 (2024)

Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses

Apprentissage statistique inspiré par la physique - Principes et application à la prévision d’énergie photovoltaïque
V. Le Guen
Techniques de l'Ingénieur (2024)

Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses
M. Schouler, R.-A. Caulk, L. Meyer, T. Terraz, C. Conrads, S. Friedermann, A. Agarwal, J.-M. Baldonado, B. Pogodziński, A. Sekuła, A. Ribes, B. Raffin
Journal of Open Source Software 8(86): 5291 (2023)

Towards new formal rules for informative prior elicitation?
N. Bousquet
Applied Stochastic Models in Business and Industry : 1-11 (2023)

High Throughput Training of Deep Surrogates from Large Ensemble Runs
L. Meyer, M. Schouler, R.-A. Caulk, A. Ribes, B. Raffin
Proceedings of The International Conference for High Performance Computing (Supercomputing), Networking, Storage, and Analysis, Denver, Colorado, USA, November 2023

On the coalitional decomposition of parameters of interest
M. Il Idrissi, N. Bousquet, F. Gamboa, B. Iooss, J.-M. Loubes
Comptes-Rendus de l'Academie des Sciences , 361: 1653-1662 (2023)

Shapley effects and proportional marginal effects for global sensitivity analysis: application to computed tomography scan organ dose estimation,
A. Foucault, M. Il Idrissi, B. Iooss, M.-O. Bernier, S. Ancelet
submitted (2023)

An overview of variance-based importance measures in the linear regression context: comparative analyses and numerical tests
L. Clouvel, B. Iooss, V. Chabridon, M. Il Idrissi, F. Robin
Socio-Environmental Systems Modelling (SESMO), 7: 18681 (2025)

Associated package ShapKit

Une méthode d'approximation des effets de Shapley en grande dimension
B. Iooss, L. Clouvel
Actes des Journées de Statistique (2023)

TSCFKit and CFKit: two Python modules dedicated to counterfactual examples
C.B. Huynh, V. Thouvenot
Actes des Journées de Statistique (2023)

Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
N. Acharki, J. Garnier, A. Bertoncello, R. Lugo
Proceedings of ICML (2023)

Proportional marginal effects for global sensitivity analysis
M. Hérin, M. Il Idrissi, V. Chabridon, B. Iooss
SIAM/ASA Journal of Uncertainty Quantification, 12:667-692 (2024)

Associated R gitlab sensitivity

Associated Python gitlab PME

Training Deep Surrogate Models with Large Scale Online Learning
L. Meyer, M. Schouler, R.A. Caulk, A. Ribes, B. Raffin
Proceedings of ICML (2022)

Explanation of Pseudo-Boolean Functions using Cooperative Game Theory and Prime Implicants
C. Labreuche
International Conference on Scalable Uncertainty Management (SUM 2022) (2022)

Residual Model-Based Reinforcement Learning for Physical Dynamics
Z. El Asri, C. Rambour, V. Le Guen, N. Thome
Proceedings of the Offline Reinforcement Learning NeurIPS Workshop, (2022), New Orleans, USA.

Simulation-Based Parallel Training
L. Meyer, A. Ribes, B. Raffin
Proceedings of the AI for Science NeurIPS Workshop, (2022), New Orleans, USA.

A Hybrid Reduced Basis and Machine Learning algorithm for building Surrogate Models: a first application to electromagnetism
A. Ribes, R. Persicot, L. Meyer, J.-P. Ducreux
Proceedings of the Machine Learning and the Physical Sciences NeurIPS Workshop, (2022), New Orleans, USA.

In Situ Monitoring and Steering Deep Learning Training from Numerical Simulations in ParaView-Catalyst
A. Ribes, F. Mazen, L. Meyer
Proceedings of the Conference on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Vizualisation (ISAV'20), (2022)

Robust Prediction Interval Estimation for Gaussian Processes By Cross-Validation Method
N. Acharki, A. Bertoncello, J. Garnier
Computational Statistics & Data Analysis, 178: 107597 (2022)

Different views of interpretability
B. Iooss, R. Kennet, P. Secchi
In: Interpretability for Industry 4.0: Statistical and Machine Learning Approaches , A. Lepore, B. Palumbo and J-M. Poggi (Eds) Springer (2022)

Variance-based importance measures for machine learning model interpretability
B. Iooss, V. Chabridon, V. Thouvenot
Congrès λμ23 (2022)

Projection de mesures de probabilité sous contraintes de quantile par distance de Wasserstein et approximation monotone polynomiale
M. Il Idrissi, N. Bousquet, F. Gamboa, B. Iooss, J.-M. Loubes
53èmes Journées de Statistiques de la Société Française de Statistique (SFdS) (2022)

Deep Time Series Forecasting with Shape and Temporal Criteria
V. Le Guen, N. Thome
IEEE Transactions on Pattern Analysis and Machine Intelligence , in press (2022)

On the convex hull of k-additive 0-1 capacities and its application to model identification in decision making
M. Grabisch, C. Labreuche
Fuzzy Sets and Systems , DOI:10.1016/j.fss.2022.03.018 (in press) (2022)

Visualize, Monitor and Control the Training Process of a Deep Surrogate Model in ParaView
F. Mazen, A. Schieb, A. Ribes, L. Meyer
ISC HPC 2022 Project Poster Session, Hamburg, Germany (2022)

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Y. Yin, V. Le Guen, J. Donà, E. de Bézenac, I. Ayed, N. Thome, P. Gallinari
The Ninth International Conference on Learning Representations & Journal of Statistical Mechanics: Theory and Experiment , DOI:10.1088/1742-5468/ac3ae5 (2021)

Deep Surrogate for Direct Time Fluid Dynamics
L. Meyer, L. Poittier, A. Ribes, B. Ruffin
NeurIPS 2021 Workshop "Machine Learning and the Physical Sciences" (2021)

On the Identifiability of Hierarchical Decision Models
R. Bresson, J. Cohen, E. Hüllermeier, C. Labreuche, M. Sebag
Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR 2021), Hanoi, Vietnam (2021)

Explanation with the Winter Value: Efficient Computation for Hierarchical Choquet Integrals
C. Labreuche
Proceedings of the 16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021) (Prague, Czechia): 471-485 (2021) Extended version in revision for the International Journal of Approximate Reasoning (2022)

Sample selection from a given dataset to validate machine learning models
B. Iooss
50th Meeting of the Italian Statistical Society (SIS2021): 88-93 (2021)

Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs
M. Il Idrissi, V. Chabridon, B. Iooss
Environmental Modeling & Software, 143: 105115 (2021)

Associated package review_l2tse

Mesures d'importance relative par décomposition de la performance de modèles de régression
M. Il Idrissi, B. Iooss, V. Chabridon
Actes des 52èmes Journées de la Société Française de Statistique (SFdS), Nice, Jun 2021

The Future of Sensitivity Analysis: An Essential Discipline for Systems Modeling and Policy Support
S. Razavi, A. Jakeman, A. Saltelli, C. Prieur, B. Iooss, E Borgonovo, E Plischke, S. Lo Piano, T. Iwanaga, W. Becker, S. Tarantola, J. H.A. Guillaume, J. Jakeman, H. Gupta , N. Melillo, G. Rabitti, V. Chabridon, Q. Duan, X. Sun, S. Smith, R. Sheikholeslami, N. Hosseini, M. Asadzadeh, A. Puy, S. Kucherenko, H. R. Maier
Environmental Modeling & Software, 37: 104954 (2021)

Deep Learning : des usages contrastés dans le monde socio-économique. Une contextualisation de l’ouvrage de Goodfellow, Bengio et Courville
R. Adon, F. Arthur, G. Baquiast, G. Hochard, A. Kaid Gherbi, A. Nègre, A. Simoulin, F. Talaouit- Mockli, N. Bousquet
Statistique & Société, 8: 55-108 (2021)

Causal and interpretable Rules for Time Series Analysis
A. Dhaou, A. Bertoncello, S. Gourvenec, J. Garnier, E. Le Pennec
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining: 2764–2772 (2021)

PhyDNet: Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
V. Le Guen, N. Thome
Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'20) , 11474-11484 (2020)

TS-GLR: an Adaptive Thompson Sampling for the Switching Multi-Armed Bandit Problem
R. Alami, O. Azizi
NeurIPS Worshop (2020)

A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance
G. Simon, V. Thouvenot
In: Holzinger A., Kieseberg P., Tjoa A., Weippl E. (eds)
Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science, vol 12279: 97-115 (2020)




Public open source code