Publications

DBLP GS REPEC ORCID

Works ahead!

This page is still in progress



  1. S.Stocksieker, A.Charpentier, D.Pommeret. Data Augmentation with Variational Autoencoder for Imbalanced Dataset, 2024. doi:10.48550/arXiv.2412.07039 
    • @ARTICLE{david,
   author = {Samuel Stocksieker and Arthur Charpentier and Denys Pommeret},
    title = "{Data Augmentation with Variational Autoencoder for Imbalanced Dataset}",
  journal = {ArXiv:2412.07039},
     year = 2024}
    doi arxiv github slides

  2. O.Côté, M.P.Côté, A. Charpentier, Selection bias in insurance: why portfolio-specific fairness fails to extend market-wide, 2024, doi:10.2139/ssrn.5018749 
    • @ARTICLE{DAG,
   author = {Olivier C\^ot\'e and Marie-Pierre C\^ot\'e and Arthur Charpentier},
    title = "{A Fair price to pay: exploiting causal graphs for fairness in insurance}",
  journal = {ssrn:5018749},
     year = 2024}
    doi ssrn pdf slides

  3. A.Fernandes-Machado, A.Charpentier, E.Flachaire, E.Gallic & F.Hu, Probabilistic Scores of Classifiers, Calibration is not Enough, 2024. ArXiv:2408.03421. 
    • @ARTICLE{calib2,
   author = {Agathe Fernandes-Machado and Arthur Charpentier and Emmanuel Flachaire and Ewen Gallic and Fran\c{c}ois Hu},
    title = "{ Probabilistic Scores of Classifiers, Calibration is not Enough}",
  journal = {ArXiv},
	volume = {2408.03421},
     year = 2024}
    doi arxiv github slides

  4. A.Fernandes-Machado, A.Charpentier, E.Flachaire, E.Gallic & F.Hu, From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration, 2024. ArXiv:2402.07790. 
    • @ARTICLE{calib,
   author = {Agathe Fernandes-Machado and Arthur Charpentier and Emmanuel Flachaire and Ewen Gallic and Fran\c{c}ois Hu},
    title = "{From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration}",
  journal = {ArXiv},
	volume = {2402.07790},
     year = 2024}
    doi arxiv github slides

  5. A.Fernandes Machado, F.Hu, P.Ratz, E.Gallic, A.Charpentier, Geospatial Disparities: A Case Study on Real Estate Prices in Paris, 2024. ArXiv:2401.16197 
    • @ARTICLE{calib,
   author = {Agathe Fernandes Machado and François Hu and Philipp Ratz and Ewen Gallic and Arthur Charpentier},
    title = "{Geospatial Disparities: A Case Study on Real Estate Prices in Paris}",
  journal = {ArXiv},
	volume = {2401.16197},
     year = 2024}
    doi arxiv github slides

  6. F. Hu, P. Ratz A. Charpentier, Parametric Fairness with Statistical Guarantees, 2023. doi:10.48550/arXiv.2310.20508 
    • @ARTICLE{guarantees,
   author = {Arthur Charpentier and Fran\c{c}ois Hu and Philipp Ratz},
    title = "{Parametric Fairness with Statistical Guarantees}",
  journal = {ArXiv:2310.20508},
     year = 2023}
    doi arxiv github slides

  7. F. Hu, P. Ratz A. Charpentier, Addressing Fairness and Explainability in Image Classification Using Optimal Transport, 2023, doi:10.48550/arXiv.2306.12912 
    • @ARTICLE{pics,
   author = {Arthur Charpentier and Fran\c{c}ois Hu and Philipp Ratz},
    title = "{Addressing Fairness and Explainability in Image Classification Using
 Optimal Transport}",
  journal = {ArXiv:2306.12912},
     year = 2023}
    doi arxiv github slides

  8. X.Vamparys A. Charpentier, Intelligence artificielle et individualisation des garanties en assurance: échec ou retard à l’allumage ?, 2023. 
    • @ARTICLE{xavier,
   author = {Xavier Vamparys and Arthur Charpentier},
    title = "{Intelligence artificielle et individualisation des garanties en assurance: échec ou retard à l’allumage ?}",
  journal = {Chaire Pari, Working Paper},
  volume={32},
     year = 2023}
    doi hal github slides

  9. S.Stocksieker, A.Charpentier, D.Pommeret. Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory, 2023. doi:10.48550/arXiv.2308.02966 
    • @ARTICLE{goliath,
   author = {Samuel Stocksieker and Arthur Charpentier and Denys Pommeret},
    title = "{Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory}",
  journal = {ArXiv:2308.02966},
     year = 2023}
    doi arxiv github slides

  10. A. Charpentier Quantifying fairness and discrimination in predictive models , 2022. doi:10.48550/arXiv.2212.09868 
    • @ARTICLE{fair,
   author = {Arthur Charpentier},
    title = "{Quantifying fairness and discrimination in predictive models}",
  journal = {ArXiv:2212.09868},
     year = 2022
}
    doi arxiv github slides

  11. H. Hu , A. Charpentier, M. Ghossoub, A. Schied Multiarmed Bandits Problem Under the Mean-Variance Setting , 2022. doi:10.48550/arXiv.2212.09192 
    • @ARTICLE{bandits,
   author = {Hongda Hu and Arthur Charpentier and Mario Ghossoub and Alexander Schied},
    title = "{Multiarmed Bandits Problem Under the Mean-Variance Setting}",
  journal = {ArXiv:2212.09192},
     year = 2022
}
    doi arxiv github slides

  12. M. Hassan, N. Sakr, A. Charpentier Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach , 2022. doi:10.48550/arXiv.2207.01010 
    • @ARTICLE{RL-cat,
   author = {Menna Hassan and Nourhan Sakr and Arthur Charpentier},
    title = "{Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach}",
  journal = {ArXiv:2207.01010},
     year = 2022
}
    doi arxiv github slides

  13. L. Barry, A. Charpentier L'équité de l'apprentissage machine en assurance, 2022. doi: 
    • @ARTICLE{p2p,
   author = {L. Barry and A. Charpentier},
    title = "{L'équité de l'apprentissage machine en assurance}",
  journal = {hal-03561709},
     year = 2022
}
    doi arxiv github slides

  14. L. Barry, A. Charpentier The Fairness of Machine Learning in Insurance: New Rags for an Old Man? , 2022. doi: 
    • @ARTICLE{fairLB,
   author = {L. Barry and A. Charpentier},
    title = "{The Fairness of Machine Learning in Insurance: New Rags for an Old Man? }",
  journal = {ArXiv},
     year = 2022
}
    doi arxiv github slides

  15. V. Grari, A. Charpentier, V. Lamprier, M. Detyniecki A fair pricing model via adversarial learning , 2022. doi:10.48550/arXiv.2202.12008 
    • @ARTICLE{fairVG,
   author = {V. Grary and A. Charpentier and V. Lamprier and M. Detyniecki},
    title = "{A fair pricing model via adversarial learning}",
  journal = {ArXiv},
     year = 2022,
     volume = {2202.12008}
}
    doi arxiv github slides

  16. A. Charpentier, L. Kouakou, M. Löwe, P. Ratz , F. Vermet Collaborative Insurance Sustainability and Network Structure, 2021, doi:10.48550/arXiv.2107.02764. 
    • @ARTICLE{p2p,
   author = {A. Charpentier and L. Kouakou and M. Löwe and P. Ratz and F. Vermet},
    title = "{Collaborative Insurance Sustainability and Network Structure}",
  journal = {ArXiv:2107.02764},
     year = 2021
}
    hal arxiv github slides

  17. O. Cabrignac, A. Charpentier, E. Gallic Modeling Joint Lives within Families, 2020, doi:10.48550/arXiv.2006.08446. 
    • @ARTICLE{covid,
   author = {Olivier Cabrignac and Arthur Charpentier and Ewen Gallic},
    title = "{Modeling Joint Lives within Families}",
  journal = {ArXiv},
     year = {2020}
}
    arxiv github hal slides

  18. A. Charpentier, A. Galichon, L. Vernet Optimal transport on large networks a practitioner guide, 2019, doi:10.48550/arXiv.1907.02320. 
    • @ARTICLE{Net,
   author = {Arthur Charpentier and Alfred Galichon and Lucas Vernet},
    title = "{Optimal transport on large networks a practitioner guide}",
  journal = {},
     year = 2019
}
    arxiv github pdf slides

  19. E. Belz , A. Charpentier Données Agrégées et Variables Compositionnelles : Note Méthodologique, 2019. 
    • @ARTICLE{Belz,
   author = {Enora Blez and Arthur Charpentier},
    title = "{Donn\'ees Agr\'eg\'ees et Variables Compositionnelles : Note M\'ethodologique }",
  journal = {},
     year = 2019
}
    hal arxiv pdf slides

  20. A. Charpentier, E. Flachaire Extended Scale-Free Networks, 2019, doi:10.48550/arXiv.1905.10267. 
    • @ARTICLE{options,
   author = {Arthur Charpentier and Emmanuel Flachaire},
    title = "{Extended Scale-Free Networks}",
  journal = {},
     year = 2018
}
    hal arxiv pdf slides

  21. D. Cocteau-Senn, A. Charpentier & R. Bigot La protection des données personnelles en assurance : dialogue du juriste avec l'actuaire , 2018, hal-03368606 
    • @ARTICLE{options,
   author = {},
    title = "{}",
  journal = {},
     year = 2018
}
    hal arxiv pdf slides

  22. A.D. Barry , A. Charpentier & K. Oualkacha Quantile and Expectile Regression for random effects model,2016, hal-01421752 
    • @unpublished{diogobarry:hal-01421752,
  TITLE = {{Quantile and Expectile Regression for random effects model}},
  AUTHOR = {Diogo Barry, Amadou and Charpentier, Arthur and Oualkacha, Karim},
  URL = {https://hal.archives-ouvertes.fr/hal-01421752},
  NOTE = {working paper or preprint},
  YEAR = {2016},
  MONTH = Dec,
  KEYWORDS = {quantiles ; Expectiles ; random effects ; longitudinal ; panel data},
  PDF = {https://hal.archives-ouvertes.fr/hal-01421752/file/QuantExpect.pdf},
  HAL_ID = {hal-01421752},
  HAL_VERSION = {v1},
}
    hal arxiv pdf slides

  23. M.Boudreault, A.Charpentier Multivariate integer-valued autoregressive models applied to earthquake counts, 2011, doi:10.48550/arXiv.1112.0929 
    • @ARTICLE{2011arXiv1112.0929B,
   author = {{Boudreault}, M. and {Charpentier}, A.},
    title = "{Multivariate integer-valued autoregressive models applied to earthquake counts}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1112.0929},
 primaryClass = "stat.AP",
 keywords = {Statistics - Applications, Statistics - Methodology},
     year = 2011,
    month = dec,
   adsurl = {http://adsabs.harvard.edu/abs/2011arXiv1112.0929B},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
    hal arxiv pdf slides

  24. A.Charpentier Pricing catastrophe options in incomplete markets citeseerx:10.1.1.572.4606 2008. 
    • @ARTICLE{options,
   author = {{Charpentier}, A.},
    title = "{Pricing catastrophe options in incomplete markets}",
  journal = {},
     year = 2008
}
    hal arxiv pdf slides

  25. A.Charpentier Reinsurance, ruin and solvency issues: some pitfalls, 2010, hal-00463381. 
    • @unpublished{charpentier:hal-00463381,
  TITLE = {{Reinsurance, ruin and solvency issues: some pitfalls}},
  AUTHOR = {Charpentier, Arthur},
  URL = {https://hal.archives-ouvertes.fr/hal-00463381},
  NOTE = {working paper or preprint},
  HAL_LOCAL_REFERENCE = {cahier de recherche 2010-06},
  YEAR = {2010},
  MONTH = Mar,
  KEYWORDS = {Dependence ; Reinsurance ; Ruin probability ; Solvency requirements},
  PDF = {https://hal.archives-ouvertes.fr/hal-00463381/file/wp2010-06.pdf},
  HAL_ID = {hal-00463381},
  HAL_VERSION = {v1},
}
    hal arxiv pdf slides

  26. A.Charpentier, D.Causeur Large-scale significance testing of the full Moon effect on deliveries, 2009, hal-00482743 
    • @unpublished{charpentier:hal-00482743,
  TITLE = {{Large-scale significance testing of the full Moon effect on deliveries}},
  AUTHOR = {Charpentier, Arthur and Causeur, David},
  URL = {https://hal.archives-ouvertes.fr/hal-00482743},
  NOTE = {working paper or preprint},
  YEAR = {2009},
  MONTH = Mar,
  KEYWORDS = {Number of births ; Full Moon effect ; High-dimensional data ; Multiple testing},
  PDF = {https://hal.archives-ouvertes.fr/hal-00482743/file/fullmoon.pdf},
  HAL_ID = {hal-00482743},
  HAL_VERSION = {v1},
}
    hal arxiv pdf slides



  1. O.Côté, M.P.Côté, A. Charpentier, A Fair price to pay: exploiting causal graphs for fairness in insurance, 2024, Journal of Risk & Insurance, doi:xxx 
    • @ARTICLE{DAG,
   author = {Olivier C\^ot\'e and Marie-Pierre C\^ot\'e and Arthur Charpentier},
    title = "{A Fair price to pay: exploiting causal graphs for fairness in insurance}",
  journal = {Journal of Risk and Insurance},
     year = 2024}
    doi ssrn pdf slides

  2. F. Foutel-Rodier, A. Charpentier, H. Guérin Optimal vaccination policy to prevent endemicity: a stochastic model. Journal of Mathematical Biology, 2024. doi:10.1007/s00285-024-02171-z 
    • @ARTICLE{vaccination,
   author = {F\'elix Foutel-Rodier and Arthur Charpentier and H\'el\`ene Gu\’erin},
    title = "{Optimal vaccination policy to prevent endemicity: a stochastic model}",
  journal = {Journal of Mathematical Biology},
     year = 2025}
    arxiv doi github slides

  3. K. Aas, A. Charpentier, F. Huang, R. Richman Insurance analytics: prediction, explainability and fairness. Annals of Actuarial Science, 2024. doi:10.1017/S1748499524000289 
    • @ARTICLE{editoaas,
   author = {K. Aas and A. Charpentier and F. Huang and R. Richman},
    title = "{Insurance analytics: prediction, explainability and fairness}",
  journal = {Annals of Actuarial Science},
     year = 2025}
    arxiv doi github slides

  4. A.Charpentier The role of government versus private sector provision of insurance. Journal of Risk & Insurance, 2024. doi:10.1111/jori.12497 
    • @ARTICLE{jri,
   author = {Arthur Charpentier},
    title = "{ The role of government versus private sector provision of insurance}",
  journal = {Journal of Risk \& Insurance},
     year = 2024
}
    doi arxiv pdf slides

  5. X.Vamparys, A.Charpentier Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?. Big Data & Society, 2024. doi:xxx 
    • @ARTICLE{Vamparys,
   author = {Xavier Vamparys and Arthur Charpentier},
    title = "{Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?}",
  journal = {Big Data \& Society},
     year = 2024
}
    doi arxiv pdf slides

  6. S.Stocksieker, D.Pommeret , A.Charpentier Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression. Transactions on Machine Learning Research, 2024. doi:xxx 
    • @ARTICLE{TMLR,
   author = {Samuel Stocksieker and Denys Pommeret and Arthur Charpentier},
    title = "{Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression}",
  journal = {Transactions on Machine Learning Research},
     year = 2024
}
    doi arxiv pdf slides

  7. M.Moriah, F.Vermet, A.Charpentier Measuring and Mitigating Biases in Motor Insurance Pricing.. European Actuarial Journal, 2024. doi:10.1007/s13385-024-00390-8 
    • @ARTICLE{mulah,
   author = {Mulah Moriah and Franck Vermet and Arthur Charpentier},
    title = "{ Measuring and Mitigating Biases in Motor Insurance Pricing}",
  journal = {Big Data \& Society},
     year = 2024
}
    doi arxiv pdf slides

  8. L.Barry, A.Charpentier Melting contestation: insurance fairness and machine learning. Ethics and Information Technology, 2023. doi:10.1007/s10676-023-09720-y 
    • @ARTICLE{melting2023,
   author = {Laurence Barry and Arthur Charpentier},
    title = "{Melting contestation: insurance fairness and machine learning}",
  journal = {Ethics and Information Technology},
     year = 2023
}
    doi arxiv pdf slides

  9. A.Barry , K.Oualkacha, A.Charpentier Alternative fixed-effects panel model using weighted asymmetric least squares regression . Statistical Methods & Applications, 2023. doi:10.1007/s10260-023-00692-3 
    • @ARTICLE{expectiles2023,
   author = {A. Barry and K. Oualkacha and A. Charpentier},
    title = "{Alternative fixed-effects panel model using weighted asymmetric least squares regression}",
  journal = {Statistical Methods \& Applications},
     year = 2023
}
    doi arxiv github slides

  10. L.Barry, A.Charpentier L'équité de l'apprentissage machine en assurance Statistique et Société, 2023. 
    • @ARTICLE{sets,
   author = {L. Barry and A. Charpentier},
    title = "{L'équité de l'apprentissage machine en assurance}",
  journal = {hal-03561709},
     year = 2022
}
    doi hal github slides

  11. A.Charpentier, M.James, H.Ali Predicting Drought and Subsidence Risks in France . Natural Hazards and Earth System Sciences., 2022. doi:10.5194/nhess-2021-214 
    • @ARTICLE{p2p,
   author = {A. Charpentier and M. James and H. Ali},
    title = "{Predicting Drought and Subsidence Risks in France }",
  journal = { Natural Hazards and Earth System Sciences.},
     year = 2022
}
    doi arxiv github slides

  12. A.Charpentier, E.Flachaire Pareto models for top incomes and wealth. Journal of Economic Inequality, 2021. doi:10.1007/s10888-021-09514-6 
    • @ARTICLE{ineq,
   author = {Charpentier, A. and Flachaire, M.},
    title = "{Pareto models for top incomes and wealth}",
  journal = {Journal of Economic Inequality},
     year = 2021
}
    pdf hal github slides

  13. A.Cayol, R.Bigot, A.Charpentier, Risque de pandémie, pertes d’exploitation et incertitudes des garanties assurantielles. Responsabilité civile et assurance, 2022. 
    • @ARTICLE{RCA,
   author = {Amandine Cayol, Rodolphe Bigot and Arthur Charpentier},
    title = "{Risque de pandémie, pertes d’exploitation et incertitudes des garanties assurantielles}",
  journal = {Responsabilité civile et assurance},
     year = 2022
}
    lexbase pdf github slides

  14. A.Charpentier, M.Denuit, J.Trufin Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning. Insurance: Mathematics & Economics, 2021. doi:10.1016/j.insmatheco.2021.09.001 
    • @ARTICLE{Autocalibration,
   author = {Charpentier, A. and Denuit, M. and Trufin, J.},
    title = "{Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning}",
  journal = {Insurance: Mathematics \& Economics},
     year = 2021
}
    pdf arxiv github slides

  15. A. Barry, K. Oualkacha, A. Charpentier A new GEE method to account for heteroscedasticity, using asymmetric least-square regressions. Journal of Applied Statistics 2021. doi:10.1080/02664763.2021.1957789 
    • @ARTICLE{CatNat,
   author = {A. Barry and K. Oualkacha and A. Charpentier},
    title = "{A new GEE method to account for heteroscedasticity, using asymmetric least-square regressions}",
  journal = {Journal of Applied Statistics},
     year = 2021
}
    doi arxiv git slides

  16. A. Charpentier, S. Mussard, T. Ouraga Principal Component Analysis: A Generalized Gini Approach. European Journal of Operational Research 2021.doi:10.1016/j.ejor.2021.02.010 
    • @ARTICLE{CatNat,
   author = {Charpentier, A. and Mussard, S. and Ouraga, T.},
    title = "{Principal Component Analysis: A Generalized Gini Approach}",
  journal = {European Journal of Operational Research},
     year = 2021
}
    doi arxiv git slides

  17. A. Charpentier, R. Élie, C. Remlinger Reinforcement Learning in Economics and Finance. Computational Economics, 2021.doi:10.1007/s10614-021-10119-4 
    • @ARTICLE{RL,
   author = {Arthur Charpentier and Romual \'Elie and Carl Remlinger},
    title = "{Reinforcement Learning in Economics and Finance}",
  journal = {Computational Economics},
     year = {2021}
}
    arxiv github hal slides

  18. A. Charpentier, L. Barry, M. James Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity. Geneva Paper on Risk and Insurance, 2021.doi:10.1057/s41288-021-00233-7 
    • @ARTICLE{CatNat,
   author = {Charpentier, A. and Barry, L. and Molly, J.},
    title = "{Insurance against Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity}",
  journal = {Geneva Paper on Risk \& Insurance},
     year = 2021
}
    hal doi

  19. A. Charpentier, R. Élie, M. Laurière, V.C. Tran COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability. Mathematical Modelelling of Natural Phenomena 2020.doi:10.1051/mmnp/2020045 
    • @ARTICLE{covid,
   author = {Arthur Charpentier and Romual \'Elie and Mathieu Lauri\`ere and Viet Chi Tran},
    title = "{COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability}",
  journal = {Mathematical Modelelling of Natural Phenomena},
     year = {2020}
}
    arxiv github hal slides

  20. L. Barry & A. Charpentier Personalization as a Promise: Can Big Data Change the Practice of Insurance?. Big Data & Society, 2020. doi:10.1177/2053951720935143 
    • @ARTICLE{bigdata,
   author = {Barry, L. and {Charpentier}, A.},
    title = "{Personalization as a promise: Can Big Data change the practice of insurance? }",
  journal = {Big Data \& Society},
  url = {https://journals.sagepub.com/doi/10.1177/2053951720935143},
     year = 2020
}
    hal arxiv pdf slides

  21. A. Charpentier & E. Gallic . Can historical demography benefit from the collaborative data of genealogy websites?. Population 2020 doi:10.3917/popu.2002.0391 
    • @ARTICLE{ined,
   author = {{Charpentier}, A. and Gallic, E.},
    title = "{La démographie historique peut-elle tirer profit des données collaboratives des sites de généalogie ?}",
  journal = {Population},
     year = 2020
}
    hal arxiv pdf slides

  22. A. Charpentier & E. Gallic. Using collaborative genealogy data to study migration: a research note. The History of the Family 2019, doi:10.1080/1081602X.2019.1641130 
    • @ARTICLE{}
    hal arxiv pdf slides

  23. A. Charpentier, N., Ka, S. Mussard & O.H. Ndiaye Gini Regressions and Heteroskedasticity. Econometrics, 7, 2019, doi:10.3390/econometrics7010004 
    • 
@Article{econometrics7010004,
AUTHOR = {Charpentier, Arthur and Ka, Ndéné and Mussard, Stéphane and Ndiaye, Oumar Hamady},
TITLE = {Gini Regressions and Heteroskedasticity},
JOURNAL = {Econometrics},
VOLUME = {7},
YEAR = {2019},
NUMBER = {1},
ARTICLE-NUMBER = {4},
URL = {http://www.mdpi.com/2225-1146/7/1/4},
ISSN = {2225-1146},
ABSTRACT = {We propose an Aitken estimator for Gini regression. The suggested    A-Gini estimator is proven to be a U-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized least squares and the Gini regression. A Gini-White test is proposed and shows that a better power is obtained compared with the usual White test when outlying observations contaminate the data.},
DOI = {10.3390/econometrics7010004}
}
    doi arxiv pdf slides

  24. A. Charpentier, E. Flachaire & A. Ly Econometrics and Machine Learning. Economics & Statistics 2018, doi:10.24187/ecostat.2018.505d.1970 
    • 
@article {EcoStats,
author = {Charpentier, Arthur and Flachaire, Emmanuel and Ly, Antoine},
title = {Econometrics and Machine Learning)},
journal = {Economics \& Statistics},
volume = {505},
issn = {1740-9713},
url = {https://doi.org/10.24187/ecostat.2018.505d.1970 },
doi = {10.24187/ecostat.2018.505d.1970},
pages = {147–-169},
year = {2018},
}
    doi arxiv pdf slides

  25. A. Charpentier & B. Coulmont We are not alone ! (at least, most of us). Homonymy in large scale social groups. Significance 2018, doi:10.1111/j.1740-9713.2018.01108.x 
    • 


@article {SIGN:SIGN1108,
author = {Charpentier, Arthur and Coulmont, Baptiste},
title = {We are not alone! (At least, most of us aren't)},
journal = {Significance},
volume = {15},
number = {1},
issn = {1740-9713},
url = {http://dx.doi.org/10.1111/j.1740-9713.2018.01108.x},
doi = {10.1111/j.1740-9713.2018.01108.x},
pages = {28--33},
year = {2018},
}
    doi arxiv pdf slides

  26. A. Charpentier, A. David & R. Elie Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains . Risks, 2017. 
    • @Article{risks4020012,
AUTHOR = {Charpentier, Arthur and David, Arthur and Elie, Romuald},
TITLE = {Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains },
JOURNAL = {Risks},
VOLUME = {5},
YEAR = {2017},
NUMBER = {xx},
ARTICLE NUMBER = {xx},
URL = {http://www.mdpi.com/2227-9091/},
ISSN = {xxx},
ABSTRACT = {In this paper, we investigate the impact of the claim reporting strategy of drivers, within a bonus malus system. We exhibit the induced modification of the corresponding class level transition matrix and derive the optimal reporting strategy for rational drivers. The hunger for bonuses induces optimal thresholds under which, drivers do not claim their losses. A numerical algorithm is provided for computing such thresholds and realistic numerical applications are discussed.},
DOI = {10.3390/xxxxxx}
}
    doi hal pdf slides

  27. A. Charpentier & M. Pigeon Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective) . Risks,, 4: 1-18, 2016. 
    • @@Article{risks4020012,
AUTHOR = {Charpentier, Arthur and Pigeon, Mathieu},
TITLE = {Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)},
JOURNAL = {Risks},
VOLUME = {4},
YEAR = {2016},
NUMBER = {2},
ARTICLE NUMBER = {12},
URL = {http://www.mdpi.com/2227-9091/4/2/12},
ISSN = {2227-9091},
ABSTRACT = {Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty related to reserves with “macro” and “micro” models. We study theoretical properties of econometric models (Gaussian, Poisson and quasi-Poisson) on individual data, and clustered data. Finally, applications in claims reserving are considered.},
DOI = {10.3390/risks4020012}
}
    doi arxiv pdf slides

  28. G.Geenens, A.Charpentier, D.Paindaveine Probit transformation for nonparametric kernel estimation of the copula density. Bernoulli, 23: 1848-1873, 2017. 
    • @article{geenens2017,
author = "Geenens, Gery and Charpentier, Arthur and Paindaveine, Davy",
doi = "10.3150/15-BEJ798",
fjournal = "Bernoulli",
journal = "Bernoulli",
month = "08",
number = "3",
pages = "1848--1873",
publisher = "Bernoulli Society for Mathematical Statistics and Probability",
title = "Probit transformation for nonparametric kernel estimation of the copula density",
url = "http://dx.doi.org/10.3150/15-BEJ798",
volume = "23",
year = "2017"
}
    doi arxiv pdf slides

  29. A.Charpentier, A.Galichon, M.Henry Local Utility and Multivariate Risk Aversion Mathematics of Operations Research , 41: 466-476 , 2016. 
    • @article{DBLP:journals/mor/CharpentierGH16,
  author    = {Arthur Charpentier and
               Alfred Galichon and
               Marc Henry},
  title     = {Local Utility and Multivariate Risk Aversion},
  journal   = {Math. Oper. Res.},
  volume    = {41},
  number    = {2},
  pages     = {466--476},
  year      = {2016},
  url       = {http://dx.doi.org/10.1287/moor.2015.0736},
  doi       = {10.1287/moor.2015.0736},
  timestamp = {Fri, 06 May 2016 01:00:00 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/mor/CharpentierGH16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
    doi ssrn pdf slides

  30. A.Charpentier, E.Gallic Kernel density estimation based on Ripley’s correction. Geoinformatica, 20: 95-116, 2016. 
    • @article{DBLP:journals/geoinformatica/CharpentierG16,
  author    = {Arthur Charpentier and
               Ewen Gallic},
  title     = {Kernel density estimation based on Ripley's correction},
  journal   = {GeoInformatica},
  volume    = {20},
  number    = {1},
  pages     = {95--116},
  year      = {2016},
  url       = {http://dx.doi.org/10.1007/s10707-015-0232-z},
  doi       = {10.1007/s10707-015-0232-z},
  timestamp = {Wed, 06 Jan 2016 16:25:20 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/geoinformatica/CharpentierG16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
    doi hal pdf github

  31. A.Charpentier, E.Flachaire Log-transform kernel density estimation of income distribution. Actualité Economique, 91 :141-149, 2015. 
    • @Article{RePEc:ris:actuec:0116,
  author={Charpentier, Arthur and Flachaire, Emmanuel},
  title={{Log-Transform Kernel Density Estimation Of Income Distribution}},
  journal={L'Actualité Economique},
  year=2015,
  volume={91},
  number={1-2},
  pages={141-159},
  month={Mars-Juin},
  keywords={},
  doi={},
  abstract={Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.},
  url={https://ideas.repec.org/a/ris/actuec/0116.html}
}
    hal arxiv pdf slides

  32. C. Tavéra, J.-C. Poutineau, J.-S. Pentecôte, I. Cadoret-David, A. Charpentier, C. Guéguen, M. Huchet-Bourdon, J. Licheron & G. L'Oeillet The "Mother of All Puzzles" at thirty: a meta-analysis. International Economics, 141 :80-96, 2015. 
    • @article{201580,
title = "The “mother of all puzzles” at thirty: A meta-analysis ",
journal = "International Economics ",
volume = "141",
number = "",
pages = "80 - 96",
year = "2015",
note = "",
issn = "2110-7017",
doi = "https://doi.org/10.1016/j.inteco.2015.01.001",
url = "http://www.sciencedirect.com/science/article/pii/S2110701715000062",
author = "Christophe Tavéra and Jean-Christophe Poutineau and Jean-Sébastien Pentecôte and Isabelle Cadoret and Arthur Charpentier and Chantal Gueguen and Maryline Huchet and Julien Licheron and Guillaume Loeillet and Nathalie Payelle and Sébastien Pommier",
keywords = "Feldstein–Horioka coefficient",
keywords = "Capital mobility",
keywords = "Saving-investment correlation",
keywords = "Meta-analysis ",
abstract = "Abstract This paper provides a meta-analysis of 1651 point estimates of Feldstein and Horioka saving retention coefficient from 49 peer-reviewed papers published over three decades. We get two main results. First, correcting for publication bias, we find a consistent underlying coefficient lying between 0.56 and 0.67 for studies using the original paper. Second, heterogeneity reported in the estimates of the Feldstein and Horioka can be explained by a few main factors. In particular, we find evidence that the saving retention coefficient is systematically underestimated with models written in first difference, models using the saving ratio or the current account ratio as the dependent variable instead of the investment ratio, and models including indicators of the public deficit or indicators of the country size as additional explanatory variables. "
}
    doi hal pdf slides

  33. M.T.Bastos, A.Charpentier, D.Mercea Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media. Journal of Communication, 65: 320–350, 2015. 
    • @article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
    doi arxiv pdf slides

  34. A.Charpentier, B.Le Maux Natural catastrophe insurance: How should the government intervene?. Journal of Public Economics, 115: 1-17, 2014. 
    • @article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
    doi hal pdf slides

  35. A.Charpentier, M.Durand Modeling earthquake dynamics. Journal of Seismology, 19: 721-739, 2015. 
    • @article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
    doi hal pdf slides

  36. A.Charpentier, A.-L.Fougères, C.Genest and J.G.Nešlehová Multivariate Archimax copula. Journal of Multivariate Analysis, 126 :118-136, 2014. 
    • @article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
    doi arxiv pdf slides

  37. A.Charpentier, S.Mussard Income Inequality Games Journal of Economic Inequality, 9: 529–554, 2011. 
    • @article {JCOM:JCOM12145,
author = {Bastos, Marco T. and Mercea, Dan and Charpentier, Arthur},
title = {Tents, Tweets, and Events: The Interplay Between Ongoing Protests and Social Media},
journal = {Journal of Communication},
volume = {65},
number = {2},
publisher = {Wiley Subscription Services, Inc.},
issn = {1460-2466},
url = {http://dx.doi.org/10.1111/jcom.12145},
doi = {10.1111/jcom.12145},
pages = {320--350},
keywords = {Social Media, Contentious Politics, Granger Causality Test, Occupy, Indignados, Vinegar Protests},
year = {2015},
}
    doi arxiv pdf slides

  38. A.Charpentier On the return period of the 2003 heat wave. Climatic Change, 109: 245–260, 2011. 
    • @Article{Charpentier2011,
author="Charpentier, Arthur",
title="On the return period of the 2003 heat wave",
journal="Climatic Change",
year="2011",
volume="109",
number="3",
pages="245--260",
abstract="Extremal events are difficult to model since it is difficult to characterize formally those events. The 2003 heat wave in Europe was not characterized by very high temperatures, but mainly the fact that night temperature were no cool enough for a long period of time. Hence, simulation of several models (either with heavy tailed noise or long range dependence) yield different estimations for the return period of that extremal event.",
issn="1573-1480",
doi="10.1007/s10584-010-9944-0",
url="http://dx.doi.org/10.1007/s10584-010-9944-0"
}
    doi hal pdf slides

  39. A.Charpentier, A.Oulidi Beta kernel quantile estimators of heavy-tailed loss distributions. Statistics and Computing, 20: 35–55, 2010. 
    • @Article{Charpentier2010,
author="Charpentier, Arthur
and Oulidi, Abder",
title="Beta kernel quantile estimators of heavy-tailed loss distributions",
journal="Statistics and Computing",
year="2010",
volume="20",
number="1",
pages="35--55",
abstract="In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but also for heavy tailed, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applied to Danish data on large fire insurance losses.",
issn="1573-1375",
doi="10.1007/s11222-009-9114-2",
url="http://dx.doi.org/10.1007/s11222-009-9114-2"
}
    doi arxiv pdf slides

  40. A.Charpentier, A.Oulidi Estimating allocations for value-at-risk portfolio optimization. Mathematical Methods of Operations Research, 69: 395, 2009. 
    • @Article{Charpentier2008,
author="Charpentier, Arthur
and Oulidi, Abder",
title="Estimating allocations for Value-at-Risk portfolio optimization",
journal="Mathematical Methods of Operations Research",
year="2008",
volume="69",
number="3",
pages="395",
abstract="Value-at-Risk, despite being adopted as the standard risk measure in finance, suffers severe objections from a practical point of view, due to a lack of convexity, and since it does not reward diversification (which is an essential feature in portfolio optimization). Furthermore, it is also known as having poor behavior in risk estimation (which has been justified to impose the use of parametric models, but which induces then model errors). The aim of this paper is to chose in favor or against the use of VaR but to add some more information to this discussion, especially from the estimation point of view. Here we propose a simple method not only to estimate the optimal allocation based on a Value-at-Risk minimization constraint, but also to derive---empirical---confidence intervals based on the fact that the underlying distribution is unknown, and can be estimated based on past observations.",
issn="1432-5217",
doi="10.1007/s00186-008-0244-7",
url="http://dx.doi.org/10.1007/s00186-008-0244-7"
}
    doi arxiv pdf slides

  41. A.Charpentier, J.Segers Tails of multivariate Archimedean copulas. Journal of Multivariate Analysis, 100: 1521–1537, 2009. 
    • @article{Charpentier20091521,
title = "Tails of multivariate Archimedean copulas ",
journal = "Journal of Multivariate Analysis ",
volume = "100",
number = "7",
pages = "1521 - 1537",
year = "2009",
note = "",
issn = "0047-259X",
doi = "http://doi.org/10.1016/j.jmva.2008.12.015",
url = "http://www.sciencedirect.com/science/article/pii/S0047259X08002790",
author = "Arthur Charpentier and Johan Segers",
keywords = "Archimedean copula",
keywords = "Asymptotic independence",
keywords = "Clayton copula",
keywords = "Coefficient of tail dependence",
keywords = "Complete monotonicity",
keywords = "Domain of attraction",
keywords = "Extreme value distribution",
keywords = "Frailty model",
keywords = "Regular variation",
keywords = "Survival copula",
keywords = "Tail dependence copula ",
abstract = "A complete and user-friendly directory of tails of Archimedean copulas is presented which can be used in the selection and construction of appropriate models with desired properties. The results are synthesized in the form of a decision tree: Given the values of some readily computable characteristics of the Archimedean generator, the upper and lower tails of the copula are classified into one of three classes each, one corresponding to asymptotic dependence and the other two to asymptotic independence. For a long list of single-parameter families, the relevant tail quantities are computed so that the corresponding classes in the decision tree can easily be determined. In addition, new models with tailor-made upper and lower tails can be constructed via a number of transformation methods. The frequently occurring category of asymptotic independence turns out to conceal a surprisingly rich variety of tail dependence structures. "
}
    doi arxiv pdf slides

  42. A. Charpentier, D. Sibaï Dynamic flood modeling: combining Hurst and Gumbel's approach. Environmetrics, 20: 32–52, 2009. 
    • @article {ENV:ENV909,
author = {Charpentier, Arthur and Sibaï, David},
title = {Dynamic flood modeling: combining Hurst and Gumbel's approach},
journal = {Environmetrics},
volume = {20},
number = {1},
publisher = {John Wiley & Sons, Ltd.},
issn = {1099-095X},
url = {http://dx.doi.org/10.1002/env.909},
doi = {10.1002/env.909},
pages = {32--52},
keywords = {frequency analysis, bivariate point process, ACD models},
year = {2009},
}
    doi hal pdf slides

  43. A.Charpentier Insurability of climate risks.. The Geneva Papers on Risk and Insurance, 33: 91–109, 2008. 
    • @Article{Charpentier2008,
author="Charpentier, Arthur",
title="Insurability of Climate Risks",
journal="The Geneva Papers on Risk and Insurance - Issues and Practice",
year="2008",
volume="33",
number="1",
pages="91--109",
abstract="The IPCC 2007 report noted that both the frequency and strength of hurricanes, floods and droughts have increased during the past few years. Thus, climate risk, and more specifically natural catastrophes, are now hardly insurable: losses can be huge (and the actuarial pure premium might even be infinite), diversification through the central limit theorem is not possible because of geographical correlation (a lot of additional capital is required), there might exist no insurance market since the price asked by insurance companies can be much higher than the price householders are willing to pay (short-term horizon of policyholders), and, due to climate change, there is more uncertainty (and thus additional risk). The first idea we will discuss in this paper, about insurance markets and climate risks, is that insurance exists only if risk can be transferred, not only to reinsurance companies but also to capital markets (through securitization or catastrophes options). The second one is that climate is changing, and therefore, not only prices and capital required should be important, but also uncertainty can be very large. It is extremely difficult to insure in a changing environment.",
issn="1468-0440",
doi="10.1057/palgrave.gpp.2510155",
url="http://dx.doi.org/10.1057/palgrave.gpp.2510155"
}
    doi hal pdf slides

  44. A.Charpentier Dynamic dependence ordering for Archimedean copulas and distorted copulas. Kybernetika, 44: 777-794, 2008. 
    • @article{DBLP:journals/kybernetika/Charpentier08,
  author    = {Arthur Charpentier},
  title     = {Dynamic dependence ordering for Archimedean copulas and distorted
               copulas},
  journal   = {Kybernetika},
  volume    = {44},
  number    = {6},
  pages     = {777--794},
  year      = {2008},
  url       = {http://www.kybernetika.cz/content/2008/6/777},
  timestamp = {Sun, 22 Nov 2015 09:54:47 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/kybernetika/Charpentier08},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
    doi hal pdf slides

  45. A.Charpentier, J.Segers Convergence of Archimedean copulas. Statistics and Probability Letters, 78: 412-419, 2008. 
    • @Article{RePEc:eee:stapro:v:78:y:2008:i:4:p:412-419,
  author={Charpentier, Arthur and Segers, Johan},
  title={{Convergence of Archimedean copulas}},
  journal={Statistics \& Probability Letters},
  year=2008,
  volume={78},
  number={4},
  pages={412-419},
  month={March},
  keywords={ Archimedean copula Generator Kendall distribution function},
  doi={},
  abstract={Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed.},
  url={https://ideas.repec.org/a/eee/stapro/v78y2008i4p412-419.html}
}
    doi hal pdf slides

  46. A.Charpentier, J.Segers Lower tail dependence for Archimedean copulas: Characterizations and pitfalls. Insurance: Mathematics and Economics, 40: 525-532, 2007. 
    • @Article{RePEc:eee:stapro:v:78:y:2008:i:4:p:412-419,
  author={Charpentier, Arthur and Segers, Johan},
  title={{Convergence of Archimedean copulas}},
  journal={Statistics \& Probability Letters},
  year=2008,
  volume={78},
  number={4},
  pages={412-419},
  month={March},
  keywords={ Archimedean copula Generator Kendall distribution function},
  doi={},
  abstract={Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed.},
  url={https://ideas.repec.org/a/eee/stapro/v78y2008i4p412-419.html}
}
    doi hal pdf slides

  47. A.Charpentier, A.Juri Limiting dependence structures for tail events, with applications to credit derivatives. Journal of Applied Probability, 43: 563-586, 2006. 
    • @article{charpentier_juri_2006, title={Limiting dependence structures for tail events, with applications to credit derivatives}, volume={43}, DOI={10.1017/S0021900200001832}, number={2}, journal={Journal of Applied Probability}, publisher={Cambridge University Press}, author={Charpentier, Arthur and Juri, Alessandro}, year={2006}, pages={563–586}}
    doi hal pdf slides

  48. Boüette, J.-C., Chassagneux, J.-F., Sibaï, D., Terron., R. & Charpentier, A Wind in Ireland: long memory or seasonal effect?. Stochastic Environmental Research and Risk Assessment, 20: 141, 2006. 
    • @Article{Bouette2006,
author="Bouette, Jean-Christophe
and Chassagneux, Jean-Fran{\c{c}}ois
and Sibai, David
and Terron, R{\'e}mi
and Charpentier, Arthur",
title="Wind in Ireland: long memory or seasonal effect?",
journal="Stochastic Environmental Research and Risk Assessment",
year="2006",
volume="20",
number="3",
pages="141",
abstract="Since Haslett and Raftery's paper Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource (1989), modelling meteorological time series with long memory processes, in particular the ARFIMA model has become very common. Haslett and Raftery fitted an ARFIMA model on Irish daily wind speeds. In this paper, we try to reproduce Haslett and Raftery's results (focusing on the dynamic of the wind process, and not on cross-correlation and space dependencies), and show that an ARFIMA model does not properly capture the behaviour of the series (in Modelling daily windspeed in Ireland section). Indeed, the series show a periodic behaviour, that is not taken into account by the ARFIMA model. Removing this periodic behaviour yields no results either, we therefore try to fit a GARMA model that takes into account both seasonality and long memory (in Seasonality and long memory using GARMA models section). If a GARMA process can be fitted to the data to model Irish daily data, we will show that these models could also be used to model Dutch hourly data.",
issn="1436-3259",
doi="10.1007/s00477-005-0029-y",
url="http://dx.doi.org/10.1007/s00477-005-0029-y"
}
    doi arxiv pdf slides



  1. A.Fernandes-Machado, A.Charpentier & E.Gallic, Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness, 2025. 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) ArXiv:2408.03425. 
    • @ARTICLE{transport_pgm,
   author = {Agathe Fernandes-Machado and Arthur Charpentier and Ewen Gallic},
    title = "{Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness }",
  journal = {ArXiv},
	volume = {2408.03425},
     year = 2024}
    doi arxiv github slides

  2. A.Fernandes-Machado, A.Charpentier, E.Flachaire, E.Gallic, F.Hu Post-Calibration Techniques: Balancing Calibration and Score Distribution Alignment. Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) BDU Workshop, 2024. openreview:TIy0QuWPuE 
    • @ARTICLE{neurips,
   author = {Agathe Fernandes Machado and Arthur Charpentier and Ennauel Flachaire and Ewen Gallic and Fran\c{c}ois Hu},
    title = "{Post-Calibration Techniques: Balancing Calibration and Score Distribution Alignment}",
  journal = {Thirty-Eighth Annual Conference on Neural Information Processing Systems},
     year = 2024
}
    doi arxiv pdf slides

  3. S.Stocksieker , A. Charpentier, D.Pommeret. Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets. IEEE World Congress on Computational Intelligence - IEEE WCCI, 2024. doi:10.48550/arXiv.2403.15790 
    • @ARTICLE{IEEE2024,
   author = {Samuel Stocksieker and Arthur Charpentier and Denys Pommeret},
    title = "{Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets}",
  journal = {arXiv},
	volume = {2403.15790},
     year = 2024}
    doi arxiv github mlr

  4. F. Hu, P. Ratz A. Charpentier, A Sequentially Fair Mechanism for Multiple Sensitive Attributes, AAAI-2024, 38th Annual AAAI Conference on Artificial Intelligence, 2024, doi:10.48550/arXiv.2309.06627 
    • @ARTICLE{sequential,
   author = {Arthur Charpentier and Fran\c{c}ois Hu and Philipp Ratz},
    title = "{A Sequentially Fair Mechanism for Multiple Sensitive Attributes}",
  journal = {ArXiv:2309.06627},
     year = 2023}
    doi arxiv github slides

  5. A. Charpentier, F. Hu, P. Ratz Mitigating Discrimination in Insurance with Wasserstein Barycenters. BIAS 2023, 3rd Workshop on Bias and Fairness in AI, International Workshop of ECML PKDD, 2023. doi:10.48550/arXiv.2306.12912 
    • @ARTICLE{barycenterinsurance,
   author = {Arthur Charpentier and Fran\c{c}ois Hu and Philipp Ratz},
    title = "{Mitigating Discrimination in Insurance with Wasserstein Barycenters}",
  journal = {ArXiv:2306.12912},
     year = 2023}
    doi arxiv github slides

  6. S.Stocksieker , A. Charpentier, D.Pommeret. Data Augmentation for Imbalanced Regression. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics - AISTATS, 2023. doi:10.48550/arXiv.2302.09288/ 
    • @ARTICLE{AISTATS2023,
   author = {Samuel Stocksieker and Arthur Charpentier and Denys Pommeret},
    title = "{Data Augmentation for Imbalanced Regression }",
  journal = {Proceedings of The 26th International Conference on Artificial Intelligence
and Statistics (AISTATS 2023)},
     year = 2023}
    doi arxiv github mlr

  7. F. Hu, P. Ratz , A. Charpentier, Fairness in Multi-Task Learning via Wasserstein Barycenters . Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023), 2023. doi:10.1007/978-3-031-43415-0_18 and doi:10.48550/arXiv.2306.12912 
    • @ARTICLE{barycenterinsurance,
   author = {Fran\c{c}ois Hu and Philipp Ratz and Arthur Charpentier},
    title = "{Fairness in Multi-Task Learning via Wasserstein Barycenters }",
  journal = {ArXiv:2306.10155},
     year = 2023}
    doi arxiv github slides


Books



  1. A.Charpentier, Insurance, Biases, Discrimination and Fairness. Springer 2024 
    • @Book{Springer,
author={Arthur Charpentier},
title={Insurance, Biases, Discrimination and Fairness},
year={2024},
isbn={9783031497827},
publisher={Springer Verlag}
}
    springer package

  2. G.Bénéplanc, A.Charpentier, P.Thourot Manuel d'Assurance. Presses Universitaires de France. 2022 
    • @Book{MANDASS,
author={Gilles B\'en\'eplance and Arthur Charpentier and Patrick Thourot},
title={Manuel d'Assurance},
year={2022},
isbn={9782130832935},
publisher={Presses Universitaires de France}
}
    puf website

  3. A.Charpentier Computational Actuarial Science with R. CRC Press. 2014. 
    • @Book{charpentierCAS,
author={Arthur Charpentier},
title={Computational Actuarial Science},
year={2014},
series = {The {R} series},
isbn={9781466592599},
publisher={CRC Press}
}
    crc github package

  4. A. Charpentier & C. Dutang Actuariat avec R. CRAN. 2013. 
    • @Book{charpentierCAS,
author={Arthur Charpentier and Christophe Dutang},
title={Actuariat avec R},
year={2013},
series = {},
isbn={},
url={https://cran.r-project.org/doc/contrib/Charpentier_Dutang_actuariat_avec_R.pdf}
publisher={CRAN}
}
    CRAN github package

  5. M.Denuit, A.Charpentier Mathématiques de l’assurance non-vie - Tarification et provisionnement (Tome 2). Economica. 2005. 
    • @Book{MASSNV2,
author={Michel Denuit and Arthur Charpentier},
title={Mathématiques de l’assurance non-vie - Tarification et provisionnement (Tome 2)},
year={2005},
publisher={Economica}
}
    economica English online textbook

  6. M.Denuit, A.Charpentier Mathématiques de l’assurance non-vie - Principes fondamentaux de théorie du risque (Tome 1). Economica. 2004. 
    • @Book{MASSNV,
author={Michel Denuit and Arthur Charpentier},
title={Mathématiques de l’assurance non-vie - Principes fondamentaux de théorie du risque (Tome 1)},
year={2004},
publisher={Economica}
}
    economica English online textbook

  7. Collective Actuarial Community Loss Data Analytics. An open text authored by the Actuarial Community. 2019. 
    • @Book{git,
author={},
title={},
year={2019},
series = {},
isbn={},
publisher={}
}

Reports

  1. A.Charpentier Assurance : Discrimination, biais et équité. Institut Louis Bachelier, Opinions & Débats, 25, 2022 
    • @article{ILBFr,
author={Arthur Charpentier},
title={Assurance : Discrimination, biais et équité},
year={2022},
volume={25},
Journal={Institut Louis Bachelier, Opinions \& Débats}
}
    ilb slides pdf

  2. A.Charpentier Insurance : Discrimination, biaises and fairness. Institut Louis Bachelier, Opinions & Débats, 25, 2022 
    • @article{ILBEng,
author={Arthur Charpentier},
title={Insurance : Discrimination, biaises and fairness},
year={2022},
volume={25},
Journal={Institut Louis Bachelier, Opinions \& Débats}
}
    ilb slides pdf

Chapters



  1. A. Charpentier Quantifying Fairness and Discrimination in Predictive Models. in Machine Learning for Econometrics and Related Topics, Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka Eds. Springer Nature doi:10.1007/978-3-031-43601-7_3 
    • @ARTICLE{predictive,
   author = {Arthur Charpentier},
    title = "{Quantifying Fairness and Discrimination in Predictive Models}",
  journal = {ArXiv:2212.09192},
     year = 2023
}
    doi arxiv doi github

  3. A. Charpentier, E. Flachaire, E. Gallic Optimal Transport for Counterfactual Estimation: A Method for Causal Inference . in Optimal Transport Statistics for Economics and Related Topics, Ngoc Thach, Kreinovich, Thanh Ha & Duc Trung Eds. Springer Nature doi:10.1007/978-3-031-35763-3_3 
    • @ARTICLE{counterfactual,
   author = {Arthur Charpentier and Emmanuel Flachaire and Ewen Gallic},
    title = "{Optimal Transport for Counterfactual Estimation: A Method for Causal Inference }",
  journal = {ArXiv:2212.09192},
     year = 2023
}
    doi arxiv doi github

  5. A.Charpentier, E.Flachaire Pareto Models for Risk Management. in Recent Econometric Techniques for Macroeconomic and Financial Data, G. Dufrénot & T. Matsuki Ed., 2020. 
    • @incollection{Pareto,
  author      = "Arthur Charpentier and Emmanuel Flachaire",
  title       = "Pareto Models for Risk Management",
  editor      = "Gilles Dufr\'enot and Takashi Matsuki",
  booktitle   = "Recent Econometric Techniques for Macroeconomic and Financial Data",
  publisher   = "Springer Verlag",
  year        = 2020,
  page     = xxx-xxxx
}

  6. A.Charpentier, M.Denuit On limits for machine learning algorithms in insurance. in Insurance data analytics, F.Planchet & C.Y.Robert Ed., 2020. 
    • @incollection{Pareto,
  author      = "Arthur Charpentier and Michel Denuit",
  title       = "On limits for machine learning algorithms in insurance",
  editor      = "F. Planchet and C. Robert",
  booktitle   = "Insurance data analytics",
  publisher   = "Economica",
  year        = 2020,
  page     = xxx-xxxx
}

  7. A.Charpentier Central Limit Theorem. in The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation, B. Frey Ed., 2018. 
    • @incollection{CLT,
  author      = "Arthur Charpentier",
  title       = "Central Limit Theorem",
  editor      = "Bruce Frey ",
  booktitle   = "The SAGE encyclopedia of educational research, measurement, and evaluation",
  publisher   = "SAGE",
  year        = 2018,
  page     = 255-258
}

  8. A.Charpentier Prévision avec des copules en finance. in Prévisions en Finance, Charles, Darne & Ferrara Eds., 2017. 
    • @incollection{CAS1,
  author      = "Arthur Charpentier",
  title       = "Pr\'evision avec des copules en finance",
  editor      = "Am\'elie Charles, Olivier Darne and Laurent Ferrara",
  booktitle   = "Pr\'evision en Finance",
  publisher   = "Economica",
  year        = 2017,
  chapter     = 7
}

  9. A.Charpentier, R.Kaas Introduction. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014. 
    • @incollection{CAS1,
  author      = "Arthur Charpentier and Rob Kaas",
  title       = "Introduction",
  editor      = "Charpentier, Arthur",
  booktitle   = "Computational Actuarial Science, With R",
  publisher   = "CRC Press",
  year        = 2014,
  chapter     = 1
}

  10. A.Charpentier, S.Tufféry Statistical Learning. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014. 
    • @incollection{CAS1,
  author      = "Arthur Charpentier and St\'ephane Tuff\'ery",
  title       = "Statistical Learning",
  editor      = "Charpentier, Arthur",
  booktitle   = "Computational Actuarial Science, With R",
  publisher   = "CRC Press",
  year        = 2014,
  chapter     = 6
}

  11. B.Escoto, A.Charpentier Bayesian Philosophy. in Computational Actuarial Science With R, Charpentier, A. Eds., 2014. 
    • @incollection{CAS1,
  author      = "Ben Escoto and Arthur Charpentier",
  title       = "Bayesian Philosophy",
  editor      = "Charpentier, Arthur",
  booktitle   = "Computational Actuarial Science, With R",
  publisher   = "CRC Press",
  year        = 2014,
  chapter     = 5
}

  12. A.Charpentier Modèles statistiques du risque en assurance. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014. 
    • @incollection{cop-charpentier,
  author      = "Arthur Charpentier",
  title       = "Modèles statistiques du risque en assurance",
  editor      = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
  booktitle   = "Statistique du risque",
  publisher   = "Technip",
  year        = 2014,
  pages       = "266-290",
  chapter     = 3
}

  13. A.Charpentier Copules et Risques Multiples. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014. 
    • @incollection{cop-charpentier,
  author      = "Arthur Charpentier",
  title       = "Copules et Risques Multiples",
  editor      = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
  booktitle   = "Statistique du risque",
  publisher   = "Technip",
  year        = 2014,
  pages       = "266-290",
  chapter     = 6
}

  14. A.Charpentier Mesures de Risques. in Statistique du Risque, Droesbeke, Maumy-Bertrand, Saporta & Thomas-Agnan Eds., 2014. 
    • @incollection{cop-charpentier,
  author      = "Arthur Charpentier",
  title       = "Mesures de Risques",
  editor      = "Droesbeke, Jean-Jacques and Maumy-Bertrand, Myriam and Saporta, Gilbert and Thomas-Agnan , Christine",
  booktitle   = "Statistique du risque",
  publisher   = "Technip",
  year        = 2014,
  pages       = "266-290",
  chapter     = 3
}

  15. A.Charpentier, J.D.Fermanian, O.Scaillet The estimation of copulas: Theory and practice. in Copula Methods in Derivatives and Risk Management: From Credit Risk to Market Risk, Rank Eds., 2006. 
    • @incollection{CSF,
  author      = "Arthur Charpentier and Jean-David Fermanian and Olivier Scaillet",
  title       = "The estimation of copulas: Theory and practice",
  editor      = "John Rank",
  booktitle   = "Copula Methods in Derivatives and Risk Management: From Credit Risk to Market Risk",
  publisher   = "Risk Books",
  year        = 2006,
  chapter     = 6
}


    Code

    CASDataset (R)

      
    install.packages("CASdatasets", 
	repos = "http://cas.uqam.ca/pub/", type="source")

    Equipy (python) 

      
    pip install --upgrade git+https://github.com/equilibration/equipy.git

    Review

    1. A.Charpentier La bataille de la Sécu: Une histoire du système de santé. Risques, 125, . 
      • @article{DaSilva,
author={Arthur Charpentier},
title={La bataille de la Sécu: 
Une histoire du système de santé},
year={2024}
}
      pdf Da Silva

    2. L.Barry, A.Charpentier Rapport Langreney : lutter contre le désengagement des assureurs dans la couverture des risques climatiques . Dalloz Actualité. 
      • @article{DaSilva,
author={Laurence Barry and Arthur Charpentier},
title={Rapport Langreney : lutter contre le désengagement des assureurs dans la couverture des risques climatiques
},
year={2024},
       journal={Dalloz Actualit\'e}
}
      pdf Da Silva

    3. A.Charpentier La bataille de la Sécu: Une histoire du système de santé. Risques, 125, . 
      • @article{DaSilva,
author={Arthur Charpentier},
title={La bataille de la Sécu: 
Une histoire du système de santé},
year={2024}
}
      pdf Da Silva

    4. A.Charpentier Homo Deus: le salut par l'algorithme ?. Risques, 110, . 
      • @article{Harari,
author={Arthur Charpentier},
title={Homo Deus: le salut par l'algorithme ?},
year={2017}
}
      pdf Harari

    5. A.Charpentier Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy. Risques, 108, . 
      • @article{Cathy,
author={Arthur Charpentier},
title={Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy},
year={2016}
}
      pdf O'Neil

PhD Committees

HDR Jury
Émilie Devijver 2024
Contributions to Statistical Learning for Structured Data in High-dimension
Université Grenoble Alpes
Christophe Dutang 2021
Some statistical and game-theoretic models with an actuarial perspective
Université Paris Dauphine
Nabil Kazi-Tani 2021
Contrôle stochastique, mesures de risque et théorie de la ruine : quelques problèmes de gestion des risques
Université de Lyon 1

PhD Jury
Wistan Marchadour 2024
From black to grey box : interpretability of deep network based radiomics predictive modeling
Université de Bretagne Occidentale, France
Marouane Il-Idrissi 2024
Développement de méthodes d'interprétabilité en apprentissage automatique pour la certification des intelligences artificielles reliées aux systèmes critiques
Université de Toulouse, France
Francis Duval 2024
Université du Québec à Montréal, Canada
Geoffrey Ecoto 2023
Modélisation et apprentissage machine learning appliqués à l’estimation des dommages consécutifs à la survenance d’un événement de sécheresse par retrait-gonflement des argiles dans le cadre du régime d’indemnisation des catastrophes naturelles français
Université Paris Cité, France
Eric Vansteenberghe 2023
Three essays in banking and insurance
École des Hautes Études en Sciences Sociales, France
Dafnis Krasniqi 2023
Quelques nouveaux modèles d'apprentissage statistique pour données de comptage et application en assurance
Université Paris-Sorbonne, France
Bertille Picard 2023
Counterfactuals in economics: the challenges of personalized estimation using machine learning
Aix-Marseille School of Economics, France
Lucas de Lara 2023
Counterfactual Models for Fair and Explainable Machine Learning: A Mass Transportation Approach
Université de Toulouse, France
Mohamed Ouhourane 2023
La régression asymétrique quantile et expectile en grande dimension et la sélection de variables par groupes
UQAM, Montréal, Canada
Pierre Chatelain 2023
Tarification à l’adresse en assurance habitation individuelle
Université de Lyon, France
Guillaume Boglioni Beaulieu 2022
Pairwise versus mutual independence: visualisation, actuarial applications and central limit theorems
UNSW, Australia
Meryem Yankol Schalck 2022
Investigating New Machine Learning Approaches for Financial Fraud Detection and Survival Analysis in Insurance Industries
Université d’Orléans, France
Antoine Heranval 2022
Application de méthodes d’apprentissage statistique pour l’évaluation de la nature et du coût des dommages assurés liés aux événements naturels en France
Sorbonne Université, France
Vincent Grari 2022
Adversarial mitigation to reduce unwanted biases in machine learning
Sorbonne Université, France
François Hu 2022
Semi-supervised learning in insurance: fairness and active learning
Institut Polytechnique de Paris, France
Ihsan Chaoubi 2022
Modélisation de la dépendance et apprentissage automatique
Université Laval, Québec, Canada
Enora Belz 2021
Econométrie des données imparfaites : méthodes et applications
Université de Rennes 1
Arthur Maillart 2021
Quelques méthodes d’explicabilité pour les modèles d’apprentissage statistique en actuariat.
Université de Lyon 1
Debora Zaparova 2021
Information et assurance : la segmentation des risques et la prévention dans un contexte de disponibilité des données
Université de Strasbourg
Loann Desboulets 2020
Variable selection on non-linear manifolds
Aix-Marseille School of Economics
Lenin Arango Castillo 2020
Long-Range Dependence in Stationary Gaussian Time Series: An Application to Stock Trading Volume and Realized Volatility
Queen's University
Sander Devriendt 2020
Sparse predictive modeling with applications in insurance pricing and mortality forecasting
KU Leuven
Pierrick Piette 2019
Contributions de l’Apprentissage Statistique à l’Actuariat et la Gestion des Risques Financiers
Université de Lyon 1
Oscar Alberto Quijano Xacur 2019
Computational Bayesian Methods for Insurance Premium Estimation
Concordia University, Montréal
Yang Jiao 2018
Applications of Artificial Intelligence in E-Commerce and Finance
Telecom SudParis
Edouard Debonneuil, 2018
Analyses prospectives de mortalité : approches actuarielle et biomédicale
Université de Lyon 1, France
Alexandre Godzinski, 2017
Three empirical essays on moral hazard identification in insurance
École des Hautes Études en Sciences Sociales, PSE
Fattouma Souissi, 2016
Gini-PLS regression: an application on European agriculture incomes inequalities
Université de Montpellier
Arnaud Goussebaïle, 2015
Prevention and Insurance of Natural Catastrophes
Ecole Polytechnique
Leo Guelman, 2015
Optimal personalized treatment learning models with insurance applications
Universitat de Barcelona
Przemyslaw Sloma, 2014
Contribution to the weak convergence of empirical copula processes & to the stochastic claims reserving in general insurance
Université Paris VI
Mathieu Pigeon, 2014
Multivariate Skew Models with Applications in Loss Reserving and Reinsurance
Université Catholique de Louvain
Julien Tomas, 2013
Quantifying biometric life insurance risks with nonparametric smoothing methods
Universiteit van Amsterdam
Aymric Kamega, 2011
Outils théoriques et opérationnels adaptés au contexte de l'assurance vie en Afrique subsaharienne francophone: Analyse et mesure des risques liés à la mortalité
Université Lyon I
Pierre-André Maugis, 2011
Vine copulas and conditional correlation in finance
Université Paris I Sorbonne
Tarek Zari, 2010
Contribution à l'étude du processus empirique de copule
Université Paris VI
Meriem Maatig, 2010
Effets des comportements risqués des conducteurs sur la sinistralité: Analyse empirique sur des données françaises
Université Paris II Assas
Noureddine Ben Lagha, 2008
Nouvelles approches de l'étude de la sinistralité en assurance automobile
Université Paris II Assas
PhD invitation
Charles Condevaux (Univ. Nîmes, France) Gilles Hacheme (AMSE, France) Loann Desboulets (AMSE, France) Bertille Picard (AMSE, France) Samira Ait Mekideche (University of Béjaïa, Algeria, جامعة بجاية) Fallou Niakh (ENSAE, CREST, Institut Polytechnique, France)

Follow-up PhD committee
Arthur Maillart Debora Zaparova Samuel Piveteau Bertille Picard Xavier Vamparys Mulah Moriah

MSc Committees

Francis Duval Roxane Turcotte Olivier Binette Julie Bélanger Alexandre Roy-Gaumond Paul Mathivon Mélanie Raymond Jordi Rolls Teikeu Jatsa

Reviewer

Journals (reviews)
Stochastic Environmental Research and Risk Assessment Theory and Decision Insurance: Mathematics and Economics Journal of Banking and Finance The Canadian Journal of Statistics Journal of Computational and Graphical Statistics Journal of Multivariate Analysis Communications in Statistics: Theory and Methods Quantitative Finance Journal of the American Statistical Association TEST Asia-Pacific Journal of Financial Studies Statistics and Decision Kybernetika European Journal of Finance Mathematics and Financial Economics Statistica Sinica Extremes Physics and Chemistry of the Earth Computational Statistics Geneva Papers on Risk and Insurance Bernoulli Water Ressources Statistics & Probability Letters Mathematical Finance Journal of Risk Journal of Hydrology Scandinavian Actuarial Journal Advances in Statistical Analysis European Actuarial Journal Metrika Journal of Statistical Planning and Inference Annals of Applied Statistics Constructive Approximation Econometric Reviews Annals of Economics and Statistics Annals of Actuarial Science Journal of the Royal Statistical Society – Series B Mathematics of Social Sciences Economic Theory ASTIN Bulletin Journal of Statistical Software Journal of Population Economics Risks Journal of Zhejiang University - Science A Journal of Time Series Analysis European Journal of Operation Research Econometrica Dependence Modeling Journal of Economic Behavior & Organization Entropy Sustainability Risk Analysis Journal of Statistical Computation and Simulation North American Actuarial Journal IEEE Transactions on Information Theory Remote Sensing Stochastic Processes & Applications Journal of Mathematical Economics Journal of Risk and Insurance PLOS One Journal of Theoretical Biology Bulletin de l'Association Mathématique du Québec International Journal of Mathematics in Operational Research Systems and Control Letters Applied Economics International Journal of Information Management Data Insight Big Data & Society ACM Computing Surveys Natural Hazards and Earth System Sciences; EGUsphere; Information Sciences Measurement (Journal of the International Measurement Confederation) Annals of Operations Research Information Sciences Information Sciences Finance Research Letters Neurocomputing Management Science

Books (reviews)
MIT Press Springer Verlag SAGE Economica Cambridge University Press CRC Press

Grants & Scolarships
ANR (Agence Nationale pour la Recherche, France) AXA Research Fund FNR Luxembourg CORE program (Luxembourg) FRQNT (Fonds de Recherche du Québec - Nature & Technologie, Canada) FRS-FNRS (Fonds de la Recherche Scientifique, Belgique) MICTACS (Mathematics of Information Technology and Complex systems, Canada) NSA (National Security Agency - Mathematical Sciences Grant Program - U.S.A.) NSERC-CRSNG (Natural Sciences and Engineering Research Council, Canada) IVADO (Institut de valorisation des données, Canada) ESF (European Science Foundation) ANRT (Agence Nationale Recherche & Technologie, France) ISF (הקרן הלאומית למדע, Israel) SNSF-FNS (Swiss National Science Foundation) NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek - Dutch Research Council) Research Grants Council (RGC) of Hong Kong

arthur charpentier

Faculté des Sciences (PK)
Université du Québec à Montréal
201, avenue du Président-Kennedy,
Montréal (Québec), Canada H2X 3Y7

CV pdf

charpentier.arthur@uqam.ca
freakonometrics
freakonometrics
freakonometrics
slideshare
arthur charpentier

google scholar
dllp
repec
orcid
crossref
researcher id