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Denys Pommeret

Je suis Professeur à l'Université d'Aix-Marseille

Ma page web se trouve ici : https://sites.google.com/view/denys-pommeret/home

Adresse : Aix-Marseille Université - Institut de Mathématiques de Marseille (I2M) – UMR 7373 - Site de Saint Charles, 3 place Victor Hugo, Case 19 - 13331 Marseille cédex 3

Contact : denys.pommeret@univ-amu.fr

Actualités :

Publications (assez) récentes :

  • Stocksieker, S., Pommeret, D., Charpentier, A. (2024) “Generalized Smoothed Bootstrap for Imbalanced Regression” CIKM 2024
  • Stocksieker, S., Pommeret, D., Charpentier, A. (2024) “Data Augmentation with Variational Autoencoder for Imbalanced Dataset” ICONIP 2024
  • Milhaud, X., Pommeret, D., Salhi, Y., Vandekerkhove, P. (2024) “Contamination-source based K-sample clustering” Journal of Machine Learning Research
  • Stocksieker, S., Pommeret, D., Charpentier, A. (2024) “Generalized Oversampling for Learning from Imbalanced Dataset and Associated Theory: Application in Regression” Transactions on Machine Learning research.
  • Stocksieker, S., Pommeret, D., Charpentier, A. (2024) “Boarding for ISS: Imbalanced Self-Supervised Discovery of a Scaled Autoencoder for Mixed Tabular Datasets” IEEE WCCI.
  • Mnatsakanov, R., Pommeret, D. (2024) “ On recovering the relative distribution. Part 1: The moment-recovered approach” Statistics & Probability Letters.
  • Ngounou-Bakam, Y., Pommeret, D. (2024) “Smooth test for equality of copulas” Electronic Journal of Statistics.
  • Ngounou-Bakam, Y., Pommeret, D. (2023) “Nonparametric estimation of copulas and copula densities by orthogonal projections” Econometrics and Statistics.
  • Milhaud, X., Pommeret, D., Salhi, Y., Vandekerkhove, P. (2023) “Two sample contamination model test” Bernoulli.
  • Stocksieker, S., Pommeret, D., Charpentier, A. (2023) “Data Augmentation for Imbalanced Regression” AISTATS.
  • Ngounou-Bakam, Y., Pommeret, D. (2022) “Kcop: Smooth Test for Equality of Copulas and Clustering Multivariate” R Package.
  • Fuchs, R., Thyssen, M., Creach, V., Dugenne, M., Izard, L., Latimier, M., Louchart, A., Marrec, P., Rijkeboer, M., Grégori, G., Pommeret, D. (2022) “Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks”. Limnology and Oceanography Methods.
  • Fuchs, R., Pommeret, D., Stocksieker, S. (2022) “MIAMI: MIxed data Augmentation MIxture”. ICCSA Proceedings – Lecture Notes in Computer Science.
  • Fuchs, R., Pommeret, D., Stocksieker, S. (2022) “MI2AMI: Missing data imputation using Mixed Deep Gaussian Mixture Models”. LOD Proceedings – Lecture Notes in Computer Science.
  • Pommeret, D., Reboul, L., Yao, A.F. (2022) “Testing the equality of the laws of two strictly stationary processes”. Statistical Inference for Stochastic Processes.
  • Fuchs, R., Pommeret, D., Viroli, C. (2021) “Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets”. Advances in Data Analysis and Classification.
  • Milhaud, X., Pommeret, D., Salhi, Y., Vandekerkhove, P. (2022) “Semiparametric two-sample admixture components comparison test: The symmetric case”. Journal of Statistical Planning and Inference.
  • Soubeyrand, S., Ribaud, M., Baudrot, V., Allard, D., Pommeret. D, Roques, L. (2020) “COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s)” PLOS ONE.
  • Pommeret, D., Vandekerkhove, P. (2019) “ Semiparametric density testing in the contamination model”. Electronic Journal of Statistics.
  • Doukhan, P., Grublit, I., Pommeret, D., Reboul, L. (2019) “Comparing the marginal densities of two strictly stationary linear processes”. Annals of the Institute of Statistical Mathematics.
  • Pommeret, D., Reboul, L. (2018) “Approximating the Probability Density Function of a Transformation of Random Variables”. Methodology and Computing in Applied Probability.
  • Doukhan, P., Joseph Rynkiewicz, J., Pommeret, D., Salhi, Y. (2017) “A Class of Random Field Memory Models for Mortality Forecasting”. Insurance Mathematics and Economics.
  • Goffard, P.O., Loisel, S., Pommeret, D. (2017) “Polynomial approximation for bivariate aggregate claims amount probability distributions”. Methodology and Computing in Applied Probability.
  • Goffard, P.O., Loisel, S., Pommeret, D. (2016) “A polynomial expansion to approximate the ultimate ruin probability in the coumpound Poisson ruin model”. Journal of Computational and Applied Mathematics.
  • Boutahar, M., Pommeret, D. (2016) “A test for the equality of transformations of two random variables”. ESAIM P&S.
  • Doukhan, P., Pommeret, D., Reboul, L. (2015) “Data Driven Smooth Test of Comparison of Dependent Sequences”. Journal of Multivariate Analysis.
  • Pommeret, D. (2015) “Comparing two mixing densities in nonparametric mixtures”. Sankhya
  • Pommeret, D. (2013) “A two-sample test when data are contaminated”. Statistical Methods and Applications.
  • Boutahar, M. Ghattas, B., Pommeret, D. (2013) “Nonparametric comparison of several transformations of distribution functions”. Journal of Nonparametric Statistics.
  • Baragatti, M., Grimaud, A., Pommeret, D. (2013) “Parallel tempering with Equi-Energy moves”. Statistics and Computing.
  • Baragatti, A. Grimaud, A., Pommeret, D. (2012) “Likelihood-free parallel tempering”. Statistics and Computing.
  • Druilhet, P., Pommeret, D. (2012) “Invariant conjugate analysis for exponential families”. Bayesian Analysis.
  • Baragatti, M., Pommeret, D. (2011) “A study of variable selection using g-prior distribution with ridge parameter”. Computational Statistics and Data Analysis.
  • Guyon, E., Pommeret, D. (2011) “Imputation by PLS regression for linear mixed models”. Journal de la Société Francaise de Statistique.
  • Pommeret, D. (2011) “Data driven smooth test for contaminated data”. Journal of Statistical Theory and Practice.
  • Ghattas, B., Pommeret, D., Reboul, L., Yao, A.F. (2011) “Data driven smooth test for paired populations”. Journal of Statistical Planning and Inference.
  • Baragatti, M., Pommeret, D. (2011) Comments on “Bayesian variable selection for disease classification using gene expression data”. Bioinformatics.
  • Pommeret (2010) “Testing Mixed Distributions when the Mixing Distribution Is Known”. Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification,Data Analysis, and Knowledge Organization.

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