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Gérard Biau

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Gérard Biau is a French professor whose contributions are specialized in statistics and machine learning. He studied at Mines Paris – PSL and obtained his PhD under the supervision of Alain Berlinet at Montpellier University.

After getting his accreditation to supervise research in 2003, he was appointed as full professor at Montpellier University in 2004. In 2007, he joined the Laboratoire de Probabilités, Statistique et Modélisation of Sorbonne University. He serves as the director and founding member of SCAI, the Sorbonne Center for Artificial Intelligence of Sorbonne University.[1]

He was a junior member of the Institut Universitaire de France[2] from 2012 to 2017, and served as the President of the French Statistical Society[3] from 2015 to 2018. In 2018, he was awarded the Michel-Monpetit Prize.[4] In 2024, he was appointed as a senior member of the Institut Universitaire de France,[2] and elected as a permanent member of the French Academy of Sciences.[3][5]

He is an associate editor of the statistical journals International Statistical Review (since 2009),[6] Journal of the American Statistical Association (since 2017),[7] Biometrika (since 2018),[8] and The Annals of Statistics (since 2019).[9]

Career

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His work focuses on the study of the statistical properties of artificial intelligence[10] algorithms: random forests,[11][12] functional data analysis,[13][14] gradient boosting,[15] k-nearest neighbors algorithm,[16] Generative Adversarial Networks,[17][18] recurrent neural networks,[19] and, more recently, physics-informed machine learning.[20]

He is one of the three authors of the teaching book Mathématiques et statistique pour les sciences de la nature,[21] and co-author, with Luc Devroye, of the monograph Lectures on the Nearest Neighbor Method.[22]

Awards

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  • 2003 : Prix Marie-Jeanne Laurent-Duhamel[23]
  • 2018 : Prix Michel-Monpetit[4]
  • 2023 : Forum Lecturer, 34th European Meeting of Statisticians, Warsaw[24]

References

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  1. ^ "Gérard Biau, director of SCAI, has been appointed to the Academy of Sciences". scai.sorbonne-universite.fr. Retrieved 2025-01-15.
  2. ^ a b "Les membres - Institut Universitaire de France". www.iufrance.fr. Retrieved 2025-01-31.
  3. ^ a b "Gérard Biau, directeur de SCAI, élu à l'Académie des sciences". www.sorbonne-universite.fr. Retrieved 2025-01-15.
  4. ^ a b "Laureates of the 2018 Thematical Prizes of the French Académie des Sciences | CNRS Mathématiques". www.insmi.cnrs.fr. 2018-07-14. Retrieved 2025-01-15.
  5. ^ "Les sciences et technologies du numérique mises à l'honneur à l'Académie des Sciences | Inria". www.inria.fr (in French). 2024-12-17. Retrieved 2025-01-15.
  6. ^ "onlinelibrary.com". onlinelibrary.com. Retrieved 2025-01-31.
  7. ^ "Learn about Journal of the American Statistical Association". Taylor & Francis. Retrieved 2025-01-15.
  8. ^ "Editorial_Board". Oxford Academic. Retrieved 2025-01-15.
  9. ^ "Institute of Mathematical Statistics | Annals of Statistics". Retrieved 2025-01-15.
  10. ^ "Les membres - Institut Universitaire de France". www.iufrance.fr. Retrieved 2025-01-31.
  11. ^ Biau, Gérard (2012). "Analysis of a Random Forests Model". Journal of Machine Learning Research. 13 (38): 1063–1095. ISSN 1533-7928. Retrieved 2025-01-15.
  12. ^ Biau, Gérard; Scornet, Erwan (June 2016). "A random forest guided tour". TEST. 25 (2): 197–227. arXiv:1511.05741. doi:10.1007/s11749-016-0481-7. ISSN 1133-0686. Retrieved 2025-01-15.
  13. ^ Biau, G.; Bunea, F.; Wegkamp, M.H. (June 2005). "Functional classification in Hilbert spaces". IEEE Transactions on Information Theory. 51 (6): 2163–2172. doi:10.1109/TIT.2005.847705. ISSN 1557-9654. Retrieved 2025-01-15.
  14. ^ Biau, GÉrard; Devroye, Luc; Lugosi, GÁbor (February 2008). "On the Performance of Clustering in Hilbert Spaces". IEEE Transactions on Information Theory. 54 (2): 781–790. doi:10.1109/TIT.2007.913516. ISSN 1557-9654. Retrieved 2025-01-15.
  15. ^ Biau, G.; Cadre, B.; Rouvière, L. (2019-06-01). "Accelerated gradient boosting". Machine Learning. 108 (6): 971–992. doi:10.1007/s10994-019-05787-1. ISSN 1573-0565. Retrieved 2025-01-15.
  16. ^ Biau, Gérard; Devroye, Luc (2010-11-01). "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification". Journal of Multivariate Analysis. 101 (10): 2499–2518. doi:10.1016/j.jmva.2010.06.019. ISSN 0047-259X. Retrieved 2025-01-15.
  17. ^ Biau, Gérard; Cadre, Benoît; Sangnier, Maxime; Tanielian, Ugo (2020). "Some theoretical properties of GANS". The Annals of Statistics. 48 (3): 1539–1566. arXiv:1803.07819. doi:10.1214/19-AOS1858. ISSN 0090-5364.
  18. ^ Biau, Gérard; Sangnier, Maxime; Tanielian, Ugo (2021). "Some Theoretical Insights into Wasserstein GANs". Journal of Machine Learning Research. 22 (119): 1–45. ISSN 1533-7928. Retrieved 2025-01-15.
  19. ^ Fermanian, Adeline; Marion, Pierre; Vert, Jean-Philippe; Biau, Gérard (2021). "Framing RNN as a kernel method: A neural ODE approach". Advances in Neural Information Processing Systems. 34. Curran Associates, Inc.: 3121–3134. Retrieved 2025-01-15.
  20. ^ Doumèche, Nathan; Bach, Francis; Biau, Gérard; Boyer, Claire (2024-06-30). "Physics-informed machine learning as a kernel method". Proceedings of Thirty Seventh Conference on Learning Theory. PMLR: 1399–1450. Retrieved 2025-01-15.
  21. ^ "Mathématiques et statistique pour les sciences de la nature - Modéliser, comprendre et appliquer - Gérard Biau, Jérôme Droniou, Marc Herzlich (EAN13 : 9782759808984) | La boutique EDP Sciences : e-librairie, vente en ligne de livres et ebooks scientifiques". EDP Sciences (in French). Retrieved 2025-01-15.
  22. ^ Biau, Gérard; Devroye, Luc (2015). Lectures on the Nearest Neighbor Method. Springer Series in the Data Sciences. Springer International Publishing. doi:10.1007/978-3-319-25388-6. ISBN 978-3-319-25386-2. Retrieved 2025-01-15.
  23. ^ "Le Prix Marie-Jeanne Laurent-Duhamel". www.sfds.asso.fr. Retrieved 2025-01-15.
  24. ^ "Keynotes | European Meeting of Statisticians". ems2023.org. Retrieved 2025-01-31.
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