Interests

• Extremes, rare events : Multivariate extreme value theory, dependence between rare events, censored data, max-stable processes, applications (environmental - industrial risks, anomaly detection)

• Statistical learning : Non-parametric methods, dimension reduction.

Working papers

• Aghbalou, A., Bertail, P., Portier, F., & Sabourin, A. (2022). Cross Validation for Rare Events. arXiv preprint arXiv:2202.00488 .

• Aghbalou, A. , Portier, F., Sabourin, A., Zhou, C. (2021) Tail inverse regression for dimension reduction with extreme response. arXiv preprint arXiv:2108.01432.

Publications

• Clémençon, S., Jalalzai, S. Lhaut, H., Sabourin, A., & Segers, J. (2022+). Concentration bounds for the empirical angular measure with statistical learning applications. Accepted for publication in Bernoulli arXiv preprint arXiv:2104.03966.

• Lhaut, S., Sabourin, A., & Segers, J. (2022). Uniform concentration bounds for frequencies of rare events. Statistics & Probability Letters, 189, 109610 .

• Drees, H. and Sabourin, A., Principal Component Analysis for Multivariate Extremes (2021), Electronic Journal of Statistics 15 (1), 908-943 preprint

• Jalalzai, H., Colombo, P., Clavel, C., Gaussier, E., Varni, G., Vignon, E., Sabourin, A. (2020). Heavy-tailed Representations, Text Polarity Classification \& Data Augmentation. NeurIPS 33 . link

• M. Chiapino, S. Clémençon, V. Feuillard, A. Sabourin. A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization (2019), Computational Statistics 1-22 preprint

• Jalalzai, H., Clémençon, S., Sabourin, A., On binary Classification in Extreme regions, NIPS, 2018 preprint

• Chiapino, M., Sabourin, A., Segers, J. Identifying groups of variables with the potential of being large simultaneously. Extremes, 1-30 (2018) preprint

• Achab, M., Clémençon, S., Garivier, A., Sabourin, A., & Vernade, C. (2017), Max K-armed bandit: On the ExtremeHunter algorithm and beyond proceedings of ECML-PKDD, 2017 arXiv

• S. Clémençon, A. Gramfort, A. Sabourin and A. Thomas (2017) Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere proceedings of AISTATS 2017

• Sabourin, A., Segers, J. (2017) Marginal standardization of upper semicontinuous processes. With application to max-stable processes Journal of Applied Probability 54.3 hal preprint

• Goix, N., Sabourin, A., Clémençon, S. (2017) Sparse representation of multivariate extremes with applications to anomaly detection Journal of Multivariate Analysis, 2017 hal preprint

• Chiapino, M., Sabourin, A.(2016). Feature clustering for extreme events analysis, with application to extreme stream-flow data ECML-PKDD 2016, workshop NFmcp2016 preprint

• Goix, N., Sabourin, A., Clémençon, S. (2016). Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking AISTATS 2016

• Sabourin, A., Renard, B. (2015). Combining regional estimation and historical floods: A multivariate semi-parametric approach with censored data. Water Resources reseach. preprint

• Goix, N., Sabourin, A., Clémençon, S (2015). Learning the dependence structure of rare events: a non-asymptotic study. Proceedings of the 28th Conference on Learning Theory (COLT) arXiv preprint

• Goix, N., Sabourin, A., Clémençon, S (2015). On Anomaly Ranking and Excess-Mass Curves. 18th International Conference on Artificial Intelligence and Statistics (AISTATS) arXiv preprint

• Sabourin, A. (2015). Semi-parametric modelling of excesses above high multivariate thresholds with censored data.
  Journal of Multivariate Analysis. arXiv preprint

• Sabourin, A. , Naveau, P. (2014). Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization.
  Computational Statistics and Data Analysis. HAL preprint

• Sabourin, A. , Naveau, P., Fougères, A.-L. (2013). Bayesian Model averaging for Multivariate extremes. Extremes. preprint

Packages (R)

• BMAmevt : Bayesian model averaging of angular measures for multivariate extremes.
  Available on CRAN packages repository here .

• DiriXtremes : Dirichlet Mixture model for multivariate extremes, inference with unknown number of components : implementation of a reversible-jump algorithm in a re-parametrization version of the model.
  Development version available upon request

• DiriCens : Extension of DiriXtremes for inference with censored data.
  Development version available upon request

Students

• Nathan Huet (2021 - \dots), co-advised with Stéphan Clémençon
• Anass Aghbalou (2020 - \dots), co-advised with François Portier, Patrice Bertail
• Hamid Jalalzai, (2017-2020), co-advised with Chloé Clavel
• Robin Vogel (2017-2020) co-advised with Stéphan Clémençon
• Mastane Achab (2016-2020), co-advised with Stéphan Clémençon
• Maël Chiapino ( 2014- 2018), co-advised with François Roueff.
• Nicolas Goix (2013-2016) co-advised with Stéphan Clémençon