1. Faïcel Chamroukhi. On some research and innovation challenges in AI. Talk at Trinity College Dublin., October, 2024 pdf
  2. Faïcel Chamroukhi. On some modern statistical and machine learning approaches for industrial applications. Talk at CISEM 2024 - Colloque International Statistique et Econométrie., May, 2024 pdf
  3. Faïcel Chamroukhi. On some statistical and machine learning problems in research and engineering: Challenges and industrial applications in Hybrid & Trustworthy AI. Talk at Koc University, KUIS AI Center., May, 2024 pdf
  4. Faïcel Chamroukhi, Nhat Thien Pham, Van Ha Hoang and Geoffrey J. McLachlan. Functional mixtures-of-experts. Statistics and Computing Springer., Vol. 34(3), pages:98, 2024 pdf 
    @article{chamroukhi2024iFME,
 title = {Functional mixtures-of-experts},
 author = {Fa{\"\i}cel Chamroukhi and Nhat Thien Pham and Van Ha Hoang and Geoffrey J. McLachlan},
 journal = {Statistics and Computing},
 volume = {34},
 number = {3},
 pages = {98},
 year = {2024},
 publisher = {Springer},
 url = {https://link.springer.com/article/10.1007/s11222-023-10379-0},
}
  5. Nguyen, TrungTin and Nguyen, Dung Ngoc and Nguyen, Hien Duy and Chamroukhi, Faicel. A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection. Springer Nature Singapore., pages:234–245, 2024, AI 2023: Advances in Artificial Intelligence. pdf 
    @inproceedings{Tin-ACML2023,
 author = {Nguyen, TrungTin
and Nguyen, Dung Ngoc
and Nguyen, Hien Duy
and Chamroukhi, Faicel},
 editor = {Liu, Tongliang
and Webb, Geoff
and Yue, Lin
and Wang, Dadong},
 title = {A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection},
 booktitle = {AI 2023: Advances in Artificial Intelligence},
 year = {2024},
 publisher = {Springer Nature Singapore},
 address = {Singapore},
 pages = {234--245},
 abstract = {We are motivated by the problem of identifying potentially nonlinear regression relationships between high-dimensional outputs and high-dimensional inputs of heterogeneous data. This requires regression, clustering, and model selection, simultaneously. In this framework, we apply the mixture of experts models which are among the most popular ensemble learning techniques developed in the field of neural networks. In particular, we consider a more general case of mixture of experts models characterized by multiple Gaussian experts whose means are polynomials of the input variables and whose covariance matrices have block-diagonal structures. More especially, each expert is weighted by a gating network that is a softmax function of a polynomial of the input variables. These models require several hyper-parameters, including the number of mixture components, the complexity of the softmax gating networks and Gaussian mean experts, and the hidden block-diagonal structures of the covariance matrices. We provide a non-asymptotic theory for model selection of such complex hyper-parameters using the slope heuristic approach in a penalized maximum likelihood estimation framework. Specifically, we establish a non-asymptotic risk bound on the penalized maximum likelihood estimation, which takes the form of an oracle inequality, given lower bound assumptions on the penalty function.},
 isbn = {978-981-99-8391-9},
 url = {https://hal.science/hal-03984011v3/document},
}
  6. Faïcel Chamroukhi and Thien Nhat Pham. Distributed Learning of Mixtures of Experts. arXiv:2312.09877, December, 2023 pdf 
    @article{chamroukhi2023DMoE,
 title = {Distributed Learning of Mixtures of Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham},
 year = {2023},
 month = {December},
 eprint = { arXiv:2312.09877},
 journal = {},
 url = {https://arxiv.org/pdf/2312.09877.pdf},
}
      
  7. F. Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf
  8. Faïcel Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf slides 
    @inproceedings{Chamroukhi-CMStatistics2023,
 title = {Learning mixtures-of-experts from heterogenous and high-dimensional data},
 author = {Fa{\"\i}cel Chamroukhi},
 howpublished = {CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics},
 address = {Berlin (DE)},
 year = {2023},
 month = {December},
 url = {https://hal.science/hal-04348869},
 slides = {./Talks/FChamroukhi-Talk-CMStatistics-17122023.pdf},
}
  9. F. Chamroukhi. Leveraging statistical mixtures and learning techniques in some data science problems. Journeés de Statistique Mathématiques et Data Science., November, 2023 pdf
  10. F. Chamroukhi. On some approaches of deep learning for physical simulation in Industry. AI, Data, Robotics Forum: Generative AI for European society and industry. Workshop Leveraging AI-based Physical Simulation for Industry., November, 2023 pdf
  11. P. Aknin and F. Chamroukhi. L'IA hybride. 8ème édition Data Science & Artificial Intelligence (DSAI) Day. P. Aknin and F. Chamroukhi., October, 2023 pdf
  12. F. Chamroukhi. Hybrid AI @ SystemX. Visit of the Czech AI Delegation to SystemX., October, 2023 pdf
  13. F. Chamroukhi. Chair of the Session "Generative Models 1" @ ECML PKDD 2023., September, 2023 pdf
  14. Abdelmouaiz Tebjou, Goran Frehse and Faïcel Chamroukhi. Data-driven Reachability using Christoffel Functions and Conformal Prediction., Vol. 204, pages:194–213, Sep, 2023, The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023). pdf 
    @inproceedings{tebjou-et-al-copa2023,
 title = {{Data-driven Reachability using Christoffel Functions and Conformal Prediction}},
 author = {Abdelmouaiz Tebjou and Goran Frehse and Fa{\"i}cel Chamroukhi},
 url = {https://proceedings.mlr.press/v204/tebjou23a/tebjou23a.pdf},
 booktitle = {{The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023)}},
 address = {Limassol, Cyprus},
 volume = {204},
 pages = {194--213},
 year = {2023},
 month = {Sep},
 organization = {Proceedings of Machine Learning Research},
}
  15. F. Chamroukhi. IRT SystemX: Votre transformation numérique par l'IA. La Matinale de la French Tech Paris Saclay: Les technologies IA au coeur de vos enjeux business., June, 2023 pdf
  16. F. Chamroukhi. Statistical Learning from heterogenous, high-dimensional and distributed data. Seminar Talk @ The DAVID laboratory/Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) - Université Paris Saclay., April, 2023 pdf
  17. F. Chamroukhi. On a new family of mixtures-of-experts models for learning from heterogeneous and distributed data. Heads'Up @ SystemX., Jan, 2023 pdf
  18. TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods Taylor & Francis., Vol. 52(14), pages:5048-5059, 2023 pdf 
    @article{nguyen2023approximation,
 author = {TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan},
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 journal = {Communications in Statistics - Theory and Methods},
 volume = {52},
 number = {14},
 pages = {5048-5059},
 year = {2023},
 publisher = {Taylor \& Francis},
 doi = {10.1080/03610926.2021.2002360},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
}
  19. F. Chamroukhi. Sur une nouvelle famille de mélanges d'experts pour l'apprentissage à partir de données hétérogènes à l'échelle. Seminar @ Université Polytechnique Hauts-De-France / Lab of Maths., Dec, 2022 pdf
  20. F. Chamroukhi. The industrial challenges of deploying AI in critical systems. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  21. F. Chamroukhi. Scientific challenges of Confiance.AI. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  22. F. Chamroukhi. Apprentissage statistique en vue du passage à l'echelle: représentation, inférence et sélection. Seminar @ LIPN - The Computer Science Lan of the University of Sorbonne Paris Nord, Machine Learning Team., May, 2022 pdf
  23. Faïcel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Functional Mixtures-of-Experts. arXiv preprint arXiv:2202.02249 arXiv:2202.02249, February, 2022 pdf 
    @article{chamroukhi2022fME,
 title = {Functional Mixtures-of-Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 year = {2022},
 month = {February},
 eprint = {arXiv:2202.02249},
 journal = {arXiv preprint arXiv:2202.02249},
 url = {https://arxiv.org/pdf/2202.02249.pdf},
}
  24. TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi and Florence Forbes. A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models. Electronic Journal of Statistics Institute of Mathematical Statistics and Bernoulli Society., Vol. 16(2), pages:4742–4822, 2022 pdf 
    @article{nguyen2022non-asympt-MoE,
 title = {A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models},
 author = {TrungTin Nguyen and Hien Duy Nguyen and Faicel Chamroukhi and Florence Forbes},
 journal = {Electronic Journal of Statistics},
 volume = {16},
 number = {2},
 pages = {4742--4822},
 year = {2022},
 url = {https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/A-non-asymptotic-approach-for-model-selection-via-penalization-in/10.1214/22-EJS2057.full},
 publisher = {Institute of Mathematical Statistics and Bernoulli Society},
}
  25. Faicel Chamroukhi, Segolene Brivet, Peter Savadjiev, Mark Coates and Forghani, Reza. DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation. Diagnostics, Vol. 12(12), pages:3072, 2022 pdf 
    @article{chamroukhi2022dect-clust,
 title = {DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation},
 author = {Faicel Chamroukhi and Segolene Brivet and Peter Savadjiev and  Mark Coates and Forghani, Reza},
 journal = {Diagnostics},
 volume = {12},
 number = {12},
 pages = {3072},
 year = {2022},
 url = {https://www.mdpi.com/2075-4418/12/12/3072},
}
      
  26. F. Chamroukhi. Mixtures-of-experts with functional predictors. Talk at the 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 pdf
  27. Faicel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Mixtures-of-experts with functional predictors. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 slides 
    @inproceedings{chamroukhi2021fME,
 title = {Mixtures-of-experts with functional predictors},
 author = {Faicel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CMStatistics--19-12-2021.pdf},
}
  28. Brivet Ségolène, Chamroukhi Faïcel, Coates Mark, Forghani Reza and Savadjiev Peter. Model-based clustering of Dual-Energy CT images for tumor detection. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 
    @inproceedings{chamroukhi2021fME-image,
 title = {Model-based clustering of Dual-Energy CT images for tumor detection},
 author = {Brivet S{\'e}gol{\`e}ne and Chamroukhi Fa{\"\i}cel and Coates Mark and Forghani Reza  and
Savadjiev Peter},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
}
      
  29. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien Duy and Forbes, Florence. Non-asymptotic model selection in block-diagonal mixture of polynomial experts models. arXiv preprint arXiv:2104.08959, may, 2021 pdf 
    @article{nguyen2021non-asympt-BD-MoE,
 title = {Non-asymptotic model selection in block-diagonal mixture of polynomial experts models},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien Duy and Forbes, Florence},
 eprint = {arXiv preprint arXiv:2104.08959},
 url = {https://arxiv.org/pdf/2104.08959.pdf},
 month = {may},
 year = {2021},
}
  30. F. Chamroukhi. Optimized Learning of Structured Random Systems. Talk at Luxembourg Institute of Science and Technology (LIST)., March, 2021 pdf
  31. Nguyen Hien Duy, Nguyen TrungTin, Chamroukhi Faicel and McLachlan Geoffrey John. Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications SpringerOpen., Vol. 8(1), pages:1–15, 2021 pdf 
    @article{nguyen2020approxMoE,
 title = {Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models},
 author = {Nguyen Hien Duy and Nguyen TrungTin and Chamroukhi Faicel and McLachlan Geoffrey John},
 journal = {Journal of Statistical Distributions and Applications},
 volume = {8},
 number = {1},
 pages = {1--15},
 year = {2021},
 publisher = {SpringerOpen},
 url = {https://jsdajournal.springeropen.com/track/pdf/10.1186/s40488-021-00125-0.pdf},
}
  32. F. Chamroukhi. Statistical Analysis Tools for Complex Time-Series Data. Talk hosted by R-Ladies, Tunis Section., nov, 2020 pdf
  33. F. Chamroukhi. Towards a Random Ecosystem of Data Intelligence. Talk @ The Mathematical Sciences department Auckland University of Technology., 2020 pdf
  34. Nguyen, TrungTin, Nguyen, Hien D, Chamroukhi, Faicel and McLachlan, Geoffrey J. An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models. arXiv preprint arXiv:2009.10622, 2020 pdf 
    @article{nguyen2020l_1MoE,
 title = {An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models},
 author = {Nguyen, TrungTin and Nguyen, Hien D and Chamroukhi, Faicel and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2009.10622},
 url = {https://arxiv.org/pdf/2009.10622.pdf},
 year = {2020},
}
  35. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D and McLachlan, Geoffrey J. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. arXiv preprint arXiv:2008.09787, 2020 pdf 
    @article{nguyen2020approximation,
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien D and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2008.09787},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
 year = {2020},
}
  36. T Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi and Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics & Statistics Cogent OA., Vol. 7(1), pages:1750861, 2020 pdf 
    @article{approx-by-fmmm-2020,
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 author = {T Tin Nguyen and Hien D Nguyen and Faicel Chamroukhi and Geoffrey J McLachlan},
 journal = {Cogent Mathematics \& Statistics},
 volume = {7},
 number = {1},
 pages = {1750861},
 year = {2020},
 publisher = {Cogent OA},
 url = {https://www.tandfonline.com/doi/pdf/10.1080/25742558.2020.1750861},
}
  37. F. Chamroukhi. Mixtures-of-Experts for Functional Data. Talk @ The University of Queensland, School of Mathematics and Physics., August, 2019 pdf
  38. T. Huynh and F. Chamroukhi. Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models. arXiv:1810.12161, July, 2019 pdf 
    @article{prEMME-2019,
 author = {T. Huynh and F. Chamroukhi},
 title = {Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models},
 volume = {},
 month = {July},
 year = {2019},
 url = {https://chamroukhi.com/papers/prEMME.pdf},
 eprint = { arXiv:1810.12161},
}
  39. Chamroukhi, Faïcel, Lecocq, Florian and Nguyen, Hien D. Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models., pages:42–56, July, 2019, Research School on Statistics and Data Science. pdf 
    @inproceedings{chamroukhi2019rMOGGE,
 title = {Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models},
 author = {Chamroukhi, Fa{\"\i}cel and Lecocq, Florian and Nguyen, Hien D},
 booktitle = {Research School on Statistics and Data Science},
 pages = {42--56},
 year = {2019},
 month = {July},
 organization = {Springer},
 url = {https://chamroukhi.com/papers/HD-MoGGE_Submitted},
}
  40. F. Chamroukhi. Unsupervised Statistical Learning from Heterogeneous Time-Series and Functional Data. Keynote note talk at the Research School on Statistics and Data Science-RSSDS 2019., July, 2019 pdf
  41. F. Chamroukhi. Functional Mixtures-of-Experts. Working group on Statistics & Data Sciences @ LMNO., June, 2019 pdf
  42. F. Chamroukhi. Functional Mixtures-of-Experts. Invited talk at the Model-based and multivariate functional data session, CRONOS Multivariate Data Analysis workshop., April, 2019 pdf
  43. F. Chamroukhi. Functional Mixtures-of-Experts., April, 2019, CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session. slides 
    @inproceedings{Chamroukhi-mda-2019,
 author = {F. Chamroukhi},
 title = {Functional Mixtures-of-Experts},
 booktitle = {CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session},
 address = {Limassol, Cyprius},
 year = {2019},
 month = {April},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CRONOS-MDA-2019.pdf},
}
  44. Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. arXiv:1903.00147, March, 2019 pdf 
    @article{approx-by-fmmm-2019,
 author = {Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan},
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 volume = {},
 month = {March},
 year = {2019},
 url = {https://arxiv.org/pdf/1903.00147.pdf},
 eprint = {arXiv:1903.00147},
}
  45. Faicel Chamroukhi and Bao T. Huynh. Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models. Journal de la Société Française de Statistique, Vol. 160(1), pages:57–85, March, 2019 pdf 
    @article{Chamroukhi-RMoE-2018,
 journal = {Journal de la Soci\'et\'e Fran\c caise de Statistique},
 author = {Faicel Chamroukhi and Bao T. Huynh},
 title = {{Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models}},
 volume = {160},
 number = {1},
 pages = {57--85},
 month = {March},
 year = {2019},
 url = {https://chamroukhi.com/papers/Chamroukhi_Huynh_jsfds-published.pdf},
}
     
  46. F. Chamroukhi. Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Kickoff meeting of the project ANR SMILES., February, 2019 pdf
  47. Faicel Chamroukhi and Hien D. Nguyen. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Jan., 2019 pdf 
    @article{Chamroukhi-FDA-2019,
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 author = {Faicel Chamroukhi and Hien D. Nguyen},
 doi = {10.1002/widm.1298.},
 volume = {},
 title = {Model-Based Clustering and Classification of Functional Data},
 year = {2019},
 month = {Jan.},
 url = {https://chamroukhi.com/papers/MBCC-FDA.pdf},
}
          
  48. Hien D. Nguyen, Faicel Chamroukhi and Florence Forbes. Approximation results regarding the multiple-output mixture of linear experts model. Neurocomputing, Vol. doi:10.1016/j.neucom.2019.08.014, 2019 
    @article{Nguyen-Chamroukhi-Forbes-2019,
 journal = {Neurocomputing},
 author = {Hien D. Nguyen and Faicel Chamroukhi and Florence Forbes},
 title = {Approximation results regarding the multiple-output mixture of linear experts model},
 volume = {doi:10.1016/j.neucom.2019.08.014},
 year = {2019},
}
  49. F. Chamroukhi. Statistical data science and some unsupervised learning problems. Keynote note talk @ the International Symposium on Data Science and Computational Intelligence DSCI 2018., December, 2018 pdf
  50. F. Chamroukhi. ANR SMILES. Journée de l'ANR du 29 novembre 2018: Réunion de lancements de projets du CE 40., november, 2018 pdf
  51. F. Chamroukhi. Model-based (co-)clustering in some high-dimensional scenarios. Annual Summer Session of the Working Group on Model-Based Clustering, Ann Arbor, USA., July, 2018 pdf
  52. F. Chamroukhi and B.T Huynh. Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering., July, 2018, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2018,
 author = {F. Chamroukhi and B.T Huynh},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = { },
 address = {Rio, Brazil},
 title = {Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering},
 year = {2018},
 month = {July},
 url = {https://chamroukhi.com/papers/FChamroukhi-IJCNN2018.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2018-Talk.pdf},
}
  53. Chamroukhi, F. and Huynh, Bao Tuyen. Regularized Mixture of Experts for High-Dimensional Data., June, 2018, Actes des 50 èmes Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2018,
 author = {Chamroukhi, F. and Huynh, Bao Tuyen},
 title = {Regularized Mixture of Experts for
High-Dimensional Data},
 booktitle = {Actes des 50 {\`e}mes Journ{\'e}es de Statistique},
 pages = { },
 address = {Paris},
 month = {June},
 year = {2018},
 url = {https://chamroukhi.com/papers/Chamroukhi-Huynh-JdS2018.pdf},
}
  54. F. Chamroukhi. Introduction of S4D 2018. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  55. F. Chamroukhi. On some new Mixtures-of-Experts Models. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  56. F. Chamroukhi. Unsupervised learning from high-dimensional and functional data. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  57. B.T Huynh and F. Chamroukhi. Regularised maximum-likelihood inference of mixture of experts for regression and clustering., April, 2018, Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2018,
 author = {B.T Huynh and F. Chamroukhi},
 booktitle = {Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Regularised maximum-likelihood inference of mixture of experts for regression and clustering},
 year = {2018},
 month = {April},
 url = {https://chamroukhi.com/papers/ESANN2018.pdf},
}
  58. F. Chamroukhi. Asterics: Apprentissage Statistique à l'Echelle pour la Représentation et la Classification non-supervisées. Normandie Université., 02 Oct, 2018 pdf
  59. F. Chamroukhi. SMILES: Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Talk at the days of the Normandy Federations of Mathematics (Normath) and Information Sciences and Technologies (Normastic)., 12 Oct, 2018 pdf
  60. Hien D. Nguyen and Faicel Chamroukhi. Practical and theoretical aspects of mixture-of-experts modeling: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Wiley Periodicals, Inc., pages:e1246–n/a, Feb., 2018 pdf 
    @article{NguyenChamroukhi-MoE,
 author = {Hien D. Nguyen and Faicel Chamroukhi},
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 title = {Practical and theoretical aspects of mixture-of-experts modeling: An overview},
 publisher = {Wiley Periodicals, Inc},
 issn = {1942-4795},
 doi = {10.1002/widm.1246},
 pages = {e1246--n/a},
 keywords = {classification, clustering, mixture models, mixture of experts, neural networks},
 month = {Feb.},
 year = {2018},
 url = {https://chamroukhi.com/papers/Nguyen-Chamroukhi-MoE-DMKD-2018},
}
        
  61. V. Roger, M. Ferrari, R. Marxer, F. Chamroukhi and H. Glotin. Towards the topology of autoencoder of calls versus clicks of marine mammal. The Journal of the Acoustical Society of America, Vol. 144(3), pages:1777–1778, 2018 pdf 
    @article{roger2018,
 title = {Towards the topology of autoencoder of calls versus clicks of marine mammal},
 author = {V. Roger and M. Ferrari and R. Marxer and F. Chamroukhi and H. Glotin},
 journal = {The Journal of the Acoustical Society of America},
 volume = {144},
 number = {3},
 pages = {1777--1778},
 year = {2018},
 url = {http://asa.scitation.org/doi/10.1121/1.5067859},
}
  62. V. Roger, M. Bartcus, F. Chamroukhi and Hervé Glotin. Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model. Multimedia Tools and Applications for Environmental & Biodiversity Informatics, pages:113-130, 2018 pdf 
    @article{Roger-HDP-HMM-2018,
 journal = {Multimedia Tools and Applications for Environmental \& Biodiversity Informatics},
 author = {V. Roger and M. Bartcus and F. Chamroukhi and Herv\'e Glotin},
 title = {Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model},
 volume = {},
 pages = {113-130},
 year = {2018},
 url = {https://chamroukhi.com/papers/Roger-et-al-MTAP.pdf},
}
  63. F. Chamroukhi, M. Bartcus and H. Glotin. Dirichlet Process Parsimonious Gaussian Mixture for clustering. arXiv:1501.03347v2, 2018 pdf 
    @article{Chamroukhi-DPPM-2018,
 author = {F. Chamroukhi and M. Bartcus and H. Glotin},
 eprint = {arXiv:1501.03347v2},
 publisher = {},
 title = {Dirichlet Process Parsimonious Gaussian Mixture for clustering},
 volume = {},
 url = {https://chamroukhi.com/papers/DPPM.pdf},
 year = {2018},
 note = {submitted},
}
  64. F. Chamroukhi. Model-Based Co-Clustering of High-Dimensional Functional Data. ERCIM 2017 : The 10th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2017
  65. F. Chamroukhi. Skew $t$ mixture of experts. Neurocomputing - Elsevier, Vol. 266, pages:390-408, November, 2017 pdf 
    @article{Chamroukhi-STMoE-2017,
 author = {F. Chamroukhi},
 journal = {Neurocomputing - Elsevier},
 title = {Skew $t$ mixture of experts},
 volume = {266},
 pages = {390-408},
 year = {2017},
 month = {November},
 url = {https://chamroukhi.com/papers/STMoE.pdf},
}
        
  66. F. Chamroukhi and C. Biernacki. Model-Based Co-Clustering of Multivariate Functional Data., Jul, 2017, ISI 2017 - 61st World Statistics Congress. pdf 
    @inproceedings{Chamroukhi-ISI-2017,
 author = {F. Chamroukhi and C. Biernacki},
 booktitle = {ISI 2017 - 61st World Statistics Congress},
 title = {{Model-Based Co-Clustering of Multivariate Functional Data}},
 month = {Jul},
 year = {2017},
 address = {Marrakech, Morocco},
 url = {https://chamroukhi.com/papers/ISI2017.pdf},
}
  67. F. Chamroukhi. Introduction on Sata Science. International Research Summer School in Statistics & BigData Science - SBDS 2017., june, 2017 pdf
  68. F. Chamroukhi. Sciences des données. Journée Interlabs, Pôle de Compététivité Transactions Electroniques Securisées - TES., 27 Nov, 2017 pdf
  69. F. Chamroukhi. On some statistical data analysis and learning problems in Data Science. Plenary talk at the days of the Normandy Federation of Information Sciences and Technologies (Normastic), Rouen, France., 30 Mar, 2017 pdf
  70. F. Chamroukhi. On some mixtures for modeling complex datasets. Séminaire Statistique et Sciences des données, Laboratoire de Mathématiques Nicolas Oresme (UMR LMNO)., 02 feb, 2017 pdf
  71. F. Chamroukhi. Unsupervised learning of regression mixture models with unknown number of components. Journal of Statistical Computation and Simulation Taylor & Francis Online., Vol. 86, pages:2308–2334, Nov, 2016 pdf 
    @article{Chamroukhi-RobustEMMixReg2015,
 author = {F. Chamroukhi},
 journal = {Journal of Statistical Computation and Simulation},
 title = {Unsupervised learning of regression mixture models with unknown number of components},
 volume = {86},
 pages = {2308--2334},
 month = {Nov},
 year = {2016},
 publisher = {Taylor \& Francis Online},
 url = {https://chamroukhi.com/papers/Chamroukhi-JSCS-2015.pdf},
}
      
  72. R. Jaziri, F. Chamroukhi, M. Lebbah and Y. Bennani. GTM Mixture through time for sequential data., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2016,
 author = {R. Jaziri and F. Chamroukhi and M.  Lebbah and Y. Bennani},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {GTM Mixture through time for sequential data},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-GTMTT-IJCNN2016.pdf},
}
  73. F. Chamroukhi. Skew-Normal Mixture of Experts., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-SNMoE-IJCNN-2016,
 author = {F. Chamroukhi},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {Skew-Normal Mixture of Experts},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-SNMoE-IJCNN2016.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2016-Talk.pdf},
}
        
  74. F. Chamroukhi. Bayesian mixture of spatial spline regressions., April, 2016, Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-BSSRM-ESANN-2016,
 author = {F. Chamroukhi},
 booktitle = {Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian mixture of spatial spline regressions},
 year = {2016},
 month = {April},
 url = {https://chamroukhi.com/papers/Bayes-SSRM-ESANN-2016.pdf},
}
  75. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Plenary talk at the days of the Federation of Mathematics of Normandie (FNM), Rouen, France., 06 Oct, 2016 pdf
  76. F. Chamroukhi. Statistical learning of generative models for signal processing. Seminar of the center of research in computer science, signal and automatic control of Lille, Signal, Models and Applications-SIGMA team., March 07, 2016 pdf
  77. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Caen - LMNO UMR CNRS 6139, Caen, France., March 15, 2016 pdf
  78. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Evry - LaMME UMR CNRS 8071, Evry, France., March 17, 2016 pdf
  79. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of LORIA, UMR CNRS 7503 - Nancy, France., March 23, 2016 pdf
  80. F. Chamroukhi. Hierarchical dynamical mixtures for functional data clustering and segmentation. Joint Statistics Seminar, Department of mathematics, KU Leuven, Leuven, Belgium., 01 Dec, 2016 pdf
  81. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of Institut Elie Cartan, IECL - UMR CNRS 7502 - Nancy, France., April 18, 2016 pdf
  82. F. Chamroukhi. On some mixtures for modeling complex datasets. Seminar of Inria Lille-Nord Europe, Models ofr Data Analysis and Learning (Modal) team., 12 jan, 2016 pdf
  83. F. Chamroukhi. Statistical learning of generative models for complex data analysis. Seminar of the lab of fundamental computer science of Marseille - LIF UMR CNRS 7279, Marseille, France., March 1st, 2016 pdf
  84. F. Chamroukhi. Robust mixture of experts modeling using the $t$-distribution. Neural Networks - Elsevier, Vol. 79, pages:20–36, 2016 pdf 
    @article{Chamroukhi-TMoE-2016,
 author = {F. Chamroukhi},
 journal = {Neural Networks - Elsevier},
 title = {Robust mixture of experts modeling using the $t$-distribution},
 volume = {79},
 pages = {20--36},
 year = {2016},
 url = {https://chamroukhi.com/papers/TMoE.pdf},
}
        
  85. Faicel Chamroukhi. Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation. Journal of Classification, Vol. 33(3), pages:374–411, 2016 pdf 
    @article{Chamroukhi-PWRM-2016,
 author = {Faicel Chamroukhi},
 title = {Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation},
 journal = {Journal of Classification},
 volume = {33},
 number = {3},
 pages = {374--411},
 url = {https://chamroukhi.com/papers/Chamroukhi-PWRM-JournalClassif-2016.pdf},
 year = {2016},
}
  86. F. Chamroukhi. Robust non-normal mixtures of experts. ERCIM 2015 : The 8th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2015 pdf slides
  87. F. Chamroukhi. Bayesian mixtures of spatial spline regressions. arXiv:1508.00635, Aug, 2015 pdf 
    @article{Chamroukhi-BSSRM-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1508.00635},
 title = {Bayesian mixtures of spatial spline regressions},
 volume = {},
 url = {http://arxiv.org/pdf/1508.00635.pdf},
 month = {Aug},
 year = {2015},
}
  88. F. Chamroukhi. Non-Normal Mixtures of Experts. arXiv:1506.06707, July, 2015 pdf 
    @article{Chamroukhi-NNMoE-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1506.06707},
 title = {Non-Normal Mixtures of Experts},
 volume = {},
 url = {http://arxiv.org/pdf/1506.06707.pdf},
 month = {July},
 year = {2015},
 note = {Report (61 pages)},
}
        
  89. M. Bartcus, F. Chamroukhi and H. Glotin. Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis., July, 2015, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2015,
 author = {M. Bartcus and F. Chamroukhi and H.  Glotin},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = {},
 address = {Killarney, Ireland},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis},
 year = {2015},
 month = {July},
 url = {https://chamroukhi.com/papers/IJCNN2015.pdf},
}
  90. Chamroukhi, F.. Statistical learning of latent data models for complex data analysis. Habilitation Thesis (HDR), Université de Toulon, 07 december, 2015 pdf 
    @phdthesis{Chamroukhi-HDR-2015,
 author = {Chamroukhi, F.},
 title = {Statistical learning of latent data models for complex data analysis},
 school = {Universit\'e de Toulon},
 month = {07 december},
 year = {2015},
 type = {{Habilitation Thesis (HDR)}},
 url = {https://chamroukhi.com/FChamroukhi-HDR.pdf},
}
  91. F. Chamroukhi. Statistical learning of latent data models for complex data analysis. Seminar of the lab of Mathematics Paul Painlevé, UMR CNRS 8524., 04 Nov, 2015 pdf
  92. F. Attal, M. Dedabrishvili, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Physical human activity recognition using wearable sensors. Sensors, Vol. 15(12), pages:31314-31338, 2015 pdf 
    @article{Chamroukhi-Sensors-2015,
 author = {F. Attal and M. Dedabrishvili and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 journal = {Sensors},
 title = {Physical human activity recognition using wearable sensors},
 volume = {15},
 number = {12},
 url = {https://chamroukhi.com/papers/Sensors-2015.pdf},
 pages = {31314-31338},
 year = {2015},
}
  93. F. Chamroukhi. Model-based cluster and discriminant analysis for functional data. ERCIM 2014 : The 7th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, Pisa, Italy., December, 2014 pdf
  94. F. Chamroukhi, M. Bartcus and H. Glotin. Bayesian non-parametric parsimonious Gaussian mixture for clustering., August, 2014, Proceedings of 22nd International Conference on Pattern Recognition (ICPR). pdf 
    @inproceedings{Chamroukhi-ICPR-2014,
 author = {F. Chamroukhi and M. Bartcus and H.  Glotin},
 booktitle = {Proceedings of 22nd International Conference on Pattern Recognition (ICPR)},
 pages = {},
 address = {Stockholm},
 title = {Bayesian non-parametric parsimonious Gaussian mixture for clustering},
 year = {2014},
 month = {August},
 url = {https://chamroukhi.com/papers/IPGMM-ICPR-2014.pdf},
}
  95. F. Chamroukhi. Mixture models for cluster analysis: from model-based inference to Bayesian non-parametrics. uLearnBio workshop of the International Conference on Machine Learning (ICML), Beijing., June, 2014 pdf
  96. M. Bartcus and F. Chamroukhi. Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data., June, 2014, Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML). pdf 
    @inproceedings{Chamroukhi-wkpICML-2014,
 author = {M. Bartcus and F. Chamroukhi},
 booktitle = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 pages = {},
 address = {Beijing},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data},
 year = {2014},
 month = {June},
 url = {https://chamroukhi.com/papers/ICMLwkshp_iHMM_2014.pdf},
}
  97. F. Chamroukhi, Marius Bartcus and Herve Glotin. Bayesian non-parametric parsimonious clustering., April, 2014, Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2014,
 author = {F. Chamroukhi and Marius Bartcus and Herve  Glotin},
 booktitle = {Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian non-parametric parsimonious clustering},
 year = {2014},
 month = {April},
 url = {https://chamroukhi.com/papers/IPGMM-esann2014},
 slides = {https://chamroukhi.com/conf-presentations/Friday_12h05_Chamroukhi_esann2014},
}
  98. M. Bartcus, F. Chamroukhi and H. Glotin. Clustering Bayésien Parcimonieux Non-Paramétrique., pages:3–13, Jan, 2014, Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering. pdf 
    @inproceedings{Chamroukhi-EGC-2014,
 author = {M. Bartcus and F. Chamroukhi and H. Glotin},
 title = {Clustering Bay{\'e}sien Parcimonieux Non-Param{\'e}trique},
 booktitle = {Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering},
 pages = {3--13},
 address = {Rennes, France},
 month = {Jan},
 year = {2014},
 url = {https://chamroukhi.com/papers/IPGMM-EGC-CluCo-2014.pdf},
}
  99. Chamroukhi, F. and Glotin, H.. Unsupervised learning from big bioacoustic data (uLearnBio). Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)., 2014 
    @book{ProceedingsUlearnBioICML2014,
 author = {Chamroukhi, F. and Glotin, H.},
 title = {Unsupervised learning from big bioacoustic data (uLearnBio)},
 publisher = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 year = {2014},
 address = {Beijing, China},
 isbn = {979-10-90821-06-4},
 note = {\href{http://sabiod.univ-tln.fr/ulearnbio/}{home page}},
}
  100. F. Chamroukhi, D. Trabelsi, S. Mohammed, L. Oukhellou and Y. Amirat. Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing Elsevier., Vol. 120, pages:633–644, November, 2013 pdf 
    @article{Chamroukhi-MRHLP-2013,
 author = {F. Chamroukhi and D. Trabelsi and S. Mohammed and L. Oukhellou and Y. Amirat},
 journal = {Neurocomputing},
 month = {November},
 pages = {633--644},
 publisher = {Elsevier},
 title = {Joint segmentation of multivariate time series with hidden process regression for human activity recognition},
 volume = {120},
 year = {2013},
 note = {},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013b.pdf},
}
        
  101. F. Chamroukhi. Robust EM algorithm for model-based curve clustering., pages:1–8, August, 2013, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2013,
 author = {F. Chamroukhi},
 title = {Robust {EM} algorithm for model-based curve clustering},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Dallas, Texas},
 month = {August},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-Robust-EM-MixReg-IJCNN2013.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
}
      
  102. F. Chamroukhi, H. Glotin and A. Samé. Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing, Vol. 112, pages:153-163, July, 2013 pdf 
    @article{Chamroukhi-FMDA-neucomp2013,
 author = {F. Chamroukhi and H. Glotin and A. Sam{\'e}},
 journal = {Neurocomputing},
 pages = {153-163},
 title = {Model-based functional mixture discriminant analysis with hidden process regression for curve classification},
 volume = {112},
 year = {2013},
 month = {July},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013a.pdf},
}
          
  103. Marius Bartcus, Faicel Chamroukhi and Hervé Glotin. Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture., pages:205–211, 2013, Proceedings of the NIPS4B workshop, Neural Information Processing Systems (NIPS). pdf 
    @inproceedings{Chamroukhi-wkpNIPS-2013,
 author = {Marius Bartcus and Faicel Chamroukhi and Herv{\'e}  Glotin},
 title = {Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture},
 booktitle = {Proceedings of the NIPS4B workshop,  Neural Information Processing Systems (NIPS)},
 pages = {205--211},
 address = {Nevada, USA},
 year = {2013},
 url = {https://chamroukhi.com/papers/nips4b_wkshop.pdf},
}
  104. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression. IEEE Transactions on Automation Science and Engineering, Vol. 3(10), pages:829–335, 2013 pdf 
    @article{Chamroukhi-MHMMR-2013,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {IEEE Transactions on Automation Science and Engineering},
 number = {10},
 pages = {829--335},
 title = {An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression},
 volume = {3},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-MHMMR-IeeeTase.pdf},
}
        
  105. F. Chamroukhi. Learning probabilistic latent process models from temporal data. VIIth International Summer School ERMITES 2012 on Representations and Decisions in Cognitive Vision, La seyne/Mer., september, 2012 pdf
  106. F. Chamroukhi. Apprentissage de modèles probabilistes à processus latent à partir de données temporelles. Seminaire GFD-LIPADE Université Paris 5., july, 2012 pdf
  107. F. Chamroukhi and H. Glotin. Mixture model-based functional discriminant analysis for curve classification., pages:1–8, June, 2012, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2012,
 author = {F. Chamroukhi and H. Glotin},
 title = {Mixture model-based functional discriminant analysis for curve classification},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Brisbane, Australia},
 month = {June},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2012.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
 software = {https://github.com/fchamroukhi/MixRHLP_Matlab},
}
  108. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Supervised and unsupervised classification approaches for human activity recognition using body-mounted sensors., pages:417–422, April, 2012, Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012-2,
 author = {D. Trabelsi  and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 booktitle = {Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {417--422},
 address = {Bruges, Belgium},
 title = {Supervised and unsupervised classification approaches for human activity recognition using
body-mounted sensors},
 month = {April},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012-2.pdf},
}
  109. F. Chamroukhi, H. Glotin and C. Rabouy. Functional Mixture Discriminant Analysis with hidden process regression for curve classification., pages:281–286, April, 2012, Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012,
 author = {F. Chamroukhi and H.  Glotin and C. Rabouy},
 booktitle = {Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {281--286},
 address = {Bruges, Belgium},
 title = {Functional Mixture Discriminant Analysis with hidden process regression for curve classification},
 year = {2012},
 month = {April},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  110. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Feb, 2012 pdf
  111. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  112. F. Chamroukhi. Apprentissage de modèles probabilistes génératifs : Approches Bayésiennes Parcimonieuses pour des données fonctionnelles et des données multidimensionnelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  113. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. Classification automatique de données temporelles en classes ordonnées., Mai, 2012, Actes des 44 ème Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2012,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {Classification automatique de donn{\'e}es temporelles en classes ordonn{\'e}es},
 booktitle = {Actes des 44 {\`e}me Journ{\'e}es de Statistique},
 pages = { },
 address = {Bruxelles, Belgique},
 month = {Mai},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-jds-2012.pdf},
}
  114. Glotin, H., Chamroukhi, F. and Maillot, T.. Representations and Decisions in Cognitive Vision. Proceedings of the International Summer School ERMITES 2012., pages:507 pages, 2012 pdf 
    @book{ProceedingsErmites2012,
 author = {Glotin, H. and Chamroukhi,  F. and Maillot, T.},
 title = {Representations and Decisions in Cognitive Vision},
 publisher = {Proceedings of the International Summer School ERMITES 2012},
 year = {2012},
 pages = {507 pages},
 url = {http://sis.univ-tln.fr/~glotin/ERMITES12/Actes_ERMITES12.pdf},
 isbn = {979-10-90821-00-2},
}
  115. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Activity Recognition Using Hidden Markov Models., september, 2011, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 
    @inproceedings{Trabelsi-wkpIROS-2011,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 title = {Activity Recognition Using Hidden Markov Models},
 booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
 adress = {San Francisco, California},
 optpages = {},
 year = {2011},
 month = {september},
}
        
  116. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A dynamic probabilistic modeling of railway switches operating states., May, 2011, Proccedings of the 9th World Congress on Railway Research (WCRR). pdf 
    @inproceedings{Chamroukhi-WCRR-2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 title = {A dynamic probabilistic modeling of railway switches operating states},
 booktitle = {Proccedings of the 9th World Congress on Railway Research (WCRR)},
 address = {Lille},
 year = {2011},
 month = {May},
 url = {https://chamroukhi.com/papers/WCRR-2011-Chamroukhi.pdf},
}
  117. F. Chamroukhi. Diagnostic par analyse de données longitudinales. TISIC, Séminaire n 13, "Analyse de Données Longitudinales"., March, 2011 pdf
  118. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Modèle à processus latent et algorithme EM pour la régression non linéaire. Revue des Nouvelles Technologies de l'Information (RNTI), Vol. S1, pages:15–32, Jan, 2011 pdf 
    @article{chamroukhi_et_al_rnti2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Revue des Nouvelles Technologies de l'Information (RNTI)},
 month = {Jan},
 pages = {15--32},
 title = {Mod\`ele \`a processus latent et algorithme {EM} pour la r\'egression non lin\'eaire},
 volume = {S1},
 year = {2011},
 url = {https://chamroukhi.com/papers/chamroukhi_same_govaert_aknin_rnti.pdf},
}
        
  119. F. Chamroukhi, A. Samé, P. Aknin and G. Govaert. Model-based clustering with Hidden Markov Model regression for time series with regime changes., pages:2814–2821, Jul-Aug, 2011, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf 
    @inproceedings{Chamroukhi-IJCNN-2011,
 author = {F. Chamroukhi and A. Sam\'e  and P. Aknin and G. Govaert},
 title = {Model-based clustering with Hidden Markov Model regression for time series with regime changes},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {2814--2821},
 adress = {San Jose, California, USA},
 year = {2011},
 month = {Jul-Aug},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf},
}
        
  120. Glotin, H., Chamroukhi, F. and Paris, S.. Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. Décomposition Parcimonieuse, Contraction et Structuration des Scènes Multimodales Complexes. Proceedings of the International Summer School ERMITES 2011., pages:436 pages, 2011 pdf 
    @book{ProceedingsErmites2011,
 author = {Glotin, H. and Chamroukhi,  F. and Paris, S.},
 title = {Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. D\'ecomposition Parcimonieuse, Contraction et Structuration des Sc\`enes Multimodales Complexes},
 publisher = {Proceedings of the International Summer School ERMITES 2011},
 year = {2011},
 pages = {436 pages},
 url = {https://chamroukhi.com/books/ERMITES_2011.pdf},
 isbn = {978-29-52474-79-5},
}
  121. F. Chamroukhi. Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms. Lap Lambert Academic Publishing., 2011 
    @book{Chamroukhi_book2011,
 author = {F. Chamroukhi},
 title = {Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms},
 publisher = {Lap Lambert Academic Publishing},
 year = {2011},
 isbn = {978-3844311372},
}
  122. A. Samé, F. Chamroukhi, G. Govaert and P. Aknin. Model-based clustering and segmentation of time series with changes in regime. Advances in Data Analysis and Classification Springer Berlin / Heidelberg., Vol. 5, pages:301–321, 2011 pdf 
    @article{Chamroukhi-MixRHLP-2011,
 author = {A. Sam{\'e} and F. Chamroukhi and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 issue = {4},
 journal = {Advances in Data Analysis and Classification},
 pages = {301--321},
 publisher = {Springer Berlin / Heidelberg},
 title = {Model-based clustering and segmentation of time series with changes in regime},
 volume = {5},
 year = {2011},
 url = {https://chamroukhi.com/papers/adac-2011.pdf},
}
          
  123. F. Chamroukhi. Modèle probabiliste à base de processus latent pour la description et la classification de signaux. The Computer Science lab of Paris 13 University (LIPN)., April, 2010 pdf
  124. Chamroukhi, F.. Hidden process regression for curve modeling, classification and tracking. Ph.D. Thesis, Université de Technologie de Compiègne, 13 december, 2010 pdf 
    @phdthesis{Chamroukhi_PhD_2010,
 author = {Chamroukhi, F.},
 title = {Hidden process regression for curve modeling, classification and tracking},
 school = {Universit\'e de Technologie de Compi\`egne},
 month = {13 december},
 year = {2010},
 type = {Ph.D. Thesis},
 url = {https://chamroukhi.com/FChamroukhi-PhD.pdf},
}
  125. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing, Vol. 73(7-9), pages:1210–1221, 2010 pdf 
    @article{chamroukhi_et_al_neurocomp2010,
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Neurocomputing},
 number = {7-9},
 pages = {1210--1221},
 title = {A hidden process regression model for functional data description. Application to curve discrimination},
 volume = {73},
 year = {2010},
 url = {https://chamroukhi.com/papers/chamroukhi_neucomp_2010.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  126. F. Chamroukhi. Modélisation probabiliste à base de processus latent de signaux monodimensionnels. Business Intelligence lab, Télécom-ParisTech., October, 2009 pdf
  127. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2009 pdf
  128. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for feature extraction from time series., pages:489–496, June, 2009, International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2009,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {A regression model with a hidden logistic process for feature extraction from time series},
 booktitle = {International Joint Conference on Neural Networks (IJCNN)},
 year = {2009},
 month = {June},
 pages = {489--496},
 address = {Atlanta, GA},
 url = {https://chamroukhi.com/papers/chamroukhi_ijcnn2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_IJCNN2009},
}
          
  129. F. Chamroukhi. Modèle à processus latent pour la paramétrisation et l'apprentissage de signaux évolutifs. Application au diagnostic d'aiguillages. Séminaire DIAG, Inrets-LTN., April, 2009
  130. Samé, A., Chamroukhi, F. and Govaert, G.. Algorithme EM et modèle à processus latent pour la régression non linéaire., 2009, Actes des 41èmes Journées de Statistique de la SFDS. slides 
    @inproceedings{Chamroukhi-JDS-2009,
 author = {Sam\'e,  A. and Chamroukhi, F. and Govaert, G.},
 title = {Algorithme EM et mod\`ele \`a  processus latent pour la r\'egression non lin\'eaire},
 booktitle = {Actes des 41\`emes Journ\'ees de Statistique de la SFDS},
 pages = { },
 address = {Bordeaux},
 month = {},
 year = {2009},
 slides = {https://chamroukhi.com/conf-presentations/chamroukhi-slides-SFDS-2009.pdf},
}
          
  131. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Time series modeling by a regression approach based on a latent process. Neural Networks Elsevier Science Ltd.., Vol. 22(5-6), pages:593–602, 2009 pdf 
    @article{chamroukhi_et_al_NN2009,
 address = {Oxford, UK, UK},
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 journal = {Neural Networks},
 number = {5-6},
 pages = {593--602},
 publisher = {Elsevier Science Ltd.},
 title = {Time series modeling by a regression approach based on a latent process},
 volume = {22},
 year = {2009},
 url = {https://chamroukhi.com/papers/Chamroukhi_Neural_Networks_2009.pdf},
}
          
  132. Chamroukhi, F., Samé, A. and Aknin, P.. A probabilistic approach for the classification of railway switch operating states. IEEE., 2009, The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. 
    @inproceedings{Chamroukhi-RCM-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Aknin, P.},
 booktitle = {The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies},
 title = {A probabilistic approach for the classification of railway switch operating states},
 year = {2009},
 address = {Dublin, UK},
 publisher = {IEEE},
}
  133. Onanena, R., Chamroukhi, F., Oukhellou, L., Candusso, D., Aknin, P. and Hissel, D.. Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context. IEEE Computer Society., pages:632–637, 2009, Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA). pdf slides 
    @inproceedings{Chamroukhi-ICMLA-2009,
 author = {Onanena, R. and Chamroukhi, F. and Oukhellou, L. and Candusso, D. and Aknin, P. and Hissel, D.},
 title = {Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context},
 booktitle = {Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)},
 year = {2009},
 pages = {632--637},
 publisher = {IEEE Computer Society},
 address = {Miami, Flordia, USA},
 url = {https://chamroukhi.com/papers/IEEE-ICMLA2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ICMLA2009.pdf},
}
  134. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for signal parameterization., pages:503-508, 2009, Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Govaert, G. and Aknin, P.},
 booktitle = {Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {503-508},
 title = {A regression model with a hidden logistic process for signal parameterization},
 year = {2009},
 url = {https://chamroukhi.com/papers/chamroukhi_esann_2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ESANN2009.pdf},
}
          
  135. F. Chamroukhi. Modele de regression a processus logistique cache pour le diagnostic par suivi de point de fonctionnement des m�canismes d'aiguillages. Journée des doctorants LTN, INRETS., Nov, 2008 pdf
  136. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2008 pdf
  137. F. Chamroukhi. Régression à variable latente pour la modélisation des signaux de manøevre d'aiguillages. Journée des doctorants SPI, INRETS., July, 2008
  138. A. Samé, F. Chamroukhi and P. Aknin. Détection séquentielle de défauts sur des signaux de manøeuvres d'aiguillage., June, 2008, Workshop Surveillance, Sûreté et Sécurité des Grands Systèmes 3SGS'08. 
    @inproceedings{Chamroukhi-3SGS-2008,
 author = {A. Sam\'e and F. Chamroukhi and P. Aknin},
 title = {D\'etection s\'equentielle de d\'efauts sur des signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Workshop Surveillance, S\^uret\'e et S\'ecurit\'e des Grands Syst\`emes 3SGS'08},
 address = {Troyes, France},
 month = {June},
 year = {2008},
}
  139. Chamroukhi, F., Samé, A., Aknin, P. and Antoni, M.. Switch mechanism diagnosis using a pattern recognition approach. IEEE., pages:1–4, June, 2008, The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE). pdf slides 
    @inproceedings{Chamroukhi-RCM-2008,
 author = {Chamroukhi, F. and Sam\'e,  A. and Aknin, P. and Antoni, M.},
 title = {Switch mechanism diagnosis using a pattern recognition approach},
 booktitle = {The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE)},
 year = {2008},
 address = {Derby, UK},
 month = {June},
 pages = {1--4},
 issn = {0537-9989},
 publisher = {IEEE},
 url = {https://chamroukhi.com/papers/chamroukhi_rcm_2008.pdf},
 slides = {https://chamroukhi.com/papers/presentation_rcm_2008.pdf},
}
  140. F. Chamroukhi, A. Samé and P. Aknin. Régression à variable latente pour la modélisation de signaux de manøeuvres d'aiguillage., Vol. 112, pages:57–64, 2008, Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus sûrs, plus efficaces et plus attractifs. 
    @inproceedings{Chamroukhi-JD-INRETS-2008,
 author = {F. Chamroukhi and A. Sam\'e and P. Aknin},
 title = {R\'egression \`a variable latente pour la mod\'elisation de signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus s\^urs, plus efficaces et plus attractifs},
 volume = {112},
 pages = {57--64},
 editor = {In J. Marais and M.~Berbineau, editors},
 year = {2008},
}
  141. Chamroukhi, F.. Reconnaissance de formes pour le diagnostic et le suivi de point de fonctionnement. Master Thesis, Paris 6 university, 2007 pdf 
    @mastersthesis{Chamroukhi_2007,
 author = {Chamroukhi, F.},
 title = {Reconnaissance de formes pour le diagnostic et le suivi de point de fonctionnement},
 school = {Paris 6 university},
 year = {2007},
 type = {Master Thesis},
 address = {Paris, France},
 url = {https://chamroukhi.com/papers/chamroukhi-master-thesis.pdf},
}
  1. Faïcel Chamroukhi and Thien Nhat Pham. Distributed Learning of Mixtures of Experts. arXiv:2312.09877, December, 2023 pdf 
    @article{chamroukhi2023DMoE,
 title = {Distributed Learning of Mixtures of Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham},
 year = {2023},
 month = {December},
 eprint = { arXiv:2312.09877},
 journal = {},
 url = {https://arxiv.org/pdf/2312.09877.pdf},
}
      
  2. Faïcel Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf slides 
    @inproceedings{Chamroukhi-CMStatistics2023,
 title = {Learning mixtures-of-experts from heterogenous and high-dimensional data},
 author = {Fa{\"\i}cel Chamroukhi},
 howpublished = {CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics},
 address = {Berlin (DE)},
 year = {2023},
 month = {December},
 url = {https://hal.science/hal-04348869},
 slides = {./Talks/FChamroukhi-Talk-CMStatistics-17122023.pdf},
}
  3. F. Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf
  4. F. Chamroukhi. Leveraging statistical mixtures and learning techniques in some data science problems. Journeés de Statistique Mathématiques et Data Science., November, 2023 pdf
  5. F. Chamroukhi. On some approaches of deep learning for physical simulation in Industry. AI, Data, Robotics Forum: Generative AI for European society and industry. Workshop Leveraging AI-based Physical Simulation for Industry., November, 2023 pdf
  6. F. Chamroukhi. Hybrid AI @ SystemX. Visit of the Czech AI Delegation to SystemX., October, 2023 pdf
  7. P. Aknin and F. Chamroukhi. L'IA hybride. 8ème édition Data Science & Artificial Intelligence (DSAI) Day. P. Aknin and F. Chamroukhi., October, 2023 pdf
  8. F. Chamroukhi. Chair of the Session "Generative Models 1" @ ECML PKDD 2023., September, 2023 pdf
  9. Abdelmouaiz Tebjou, Goran Frehse and Faïcel Chamroukhi. Data-driven Reachability using Christoffel Functions and Conformal Prediction., Vol. 204, pages:194–213, Sep, 2023, The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023). pdf 
    @inproceedings{tebjou-et-al-copa2023,
 title = {{Data-driven Reachability using Christoffel Functions and Conformal Prediction}},
 author = {Abdelmouaiz Tebjou and Goran Frehse and Fa{\"i}cel Chamroukhi},
 url = {https://proceedings.mlr.press/v204/tebjou23a/tebjou23a.pdf},
 booktitle = {{The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023)}},
 address = {Limassol, Cyprus},
 volume = {204},
 pages = {194--213},
 year = {2023},
 month = {Sep},
 organization = {Proceedings of Machine Learning Research},
}
  10. F. Chamroukhi. IRT SystemX: Votre transformation numérique par l'IA. La Matinale de la French Tech Paris Saclay: Les technologies IA au coeur de vos enjeux business., June, 2023 pdf
  11. F. Chamroukhi. Statistical Learning from heterogenous, high-dimensional and distributed data. Seminar Talk @ The DAVID laboratory/Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) - Université Paris Saclay., April, 2023 pdf
  12. F. Chamroukhi. On a new family of mixtures-of-experts models for learning from heterogeneous and distributed data. Heads'Up @ SystemX., Jan, 2023 pdf
  13. TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods Taylor & Francis., Vol. 52(14), pages:5048-5059, 2023 pdf 
    @article{nguyen2023approximation,
 author = {TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan},
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 journal = {Communications in Statistics - Theory and Methods},
 volume = {52},
 number = {14},
 pages = {5048-5059},
 year = {2023},
 publisher = {Taylor \& Francis},
 doi = {10.1080/03610926.2021.2002360},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
}
  1. F. Chamroukhi. Sur une nouvelle famille de mélanges d'experts pour l'apprentissage à partir de données hétérogènes à l'échelle. Seminar @ Université Polytechnique Hauts-De-France / Lab of Maths., Dec, 2022 pdf
  2. F. Chamroukhi. The industrial challenges of deploying AI in critical systems. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  3. F. Chamroukhi. Scientific challenges of Confiance.AI. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  4. F. Chamroukhi. Apprentissage statistique en vue du passage à l'echelle: représentation, inférence et sélection. Seminar @ LIPN - The Computer Science Lan of the University of Sorbonne Paris Nord, Machine Learning Team., May, 2022 pdf
  5. Faïcel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Functional Mixtures-of-Experts. arXiv preprint arXiv:2202.02249 arXiv:2202.02249, February, 2022 pdf 
    @article{chamroukhi2022fME,
 title = {Functional Mixtures-of-Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 year = {2022},
 month = {February},
 eprint = {arXiv:2202.02249},
 journal = {arXiv preprint arXiv:2202.02249},
 url = {https://arxiv.org/pdf/2202.02249.pdf},
}
  6. TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi and Florence Forbes. A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models. Electronic Journal of Statistics Institute of Mathematical Statistics and Bernoulli Society., Vol. 16(2), pages:4742–4822, 2022 pdf 
    @article{nguyen2022non-asympt-MoE,
 title = {A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models},
 author = {TrungTin Nguyen and Hien Duy Nguyen and Faicel Chamroukhi and Florence Forbes},
 journal = {Electronic Journal of Statistics},
 volume = {16},
 number = {2},
 pages = {4742--4822},
 year = {2022},
 url = {https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/A-non-asymptotic-approach-for-model-selection-via-penalization-in/10.1214/22-EJS2057.full},
 publisher = {Institute of Mathematical Statistics and Bernoulli Society},
}
  7. Faicel Chamroukhi, Segolene Brivet, Peter Savadjiev, Mark Coates and Forghani, Reza. DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation. Diagnostics, Vol. 12(12), pages:3072, 2022 pdf 
    @article{chamroukhi2022dect-clust,
 title = {DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation},
 author = {Faicel Chamroukhi and Segolene Brivet and Peter Savadjiev and  Mark Coates and Forghani, Reza},
 journal = {Diagnostics},
 volume = {12},
 number = {12},
 pages = {3072},
 year = {2022},
 url = {https://www.mdpi.com/2075-4418/12/12/3072},
}
      
  1. Brivet Ségolène, Chamroukhi Faïcel, Coates Mark, Forghani Reza and Savadjiev Peter. Model-based clustering of Dual-Energy CT images for tumor detection. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 
    @inproceedings{chamroukhi2021fME-image,
 title = {Model-based clustering of Dual-Energy CT images for tumor detection},
 author = {Brivet S{\'e}gol{\`e}ne and Chamroukhi Fa{\"\i}cel and Coates Mark and Forghani Reza  and
Savadjiev Peter},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
}
      
  2. Faicel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Mixtures-of-experts with functional predictors. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 slides 
    @inproceedings{chamroukhi2021fME,
 title = {Mixtures-of-experts with functional predictors},
 author = {Faicel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CMStatistics--19-12-2021.pdf},
}
  3. F. Chamroukhi. Mixtures-of-experts with functional predictors. Talk at the 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 pdf
  4. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien Duy and Forbes, Florence. Non-asymptotic model selection in block-diagonal mixture of polynomial experts models. arXiv preprint arXiv:2104.08959, may, 2021 pdf 
    @article{nguyen2021non-asympt-BD-MoE,
 title = {Non-asymptotic model selection in block-diagonal mixture of polynomial experts models},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien Duy and Forbes, Florence},
 eprint = {arXiv preprint arXiv:2104.08959},
 url = {https://arxiv.org/pdf/2104.08959.pdf},
 month = {may},
 year = {2021},
}
  5. F. Chamroukhi. Optimized Learning of Structured Random Systems. Talk at Luxembourg Institute of Science and Technology (LIST)., March, 2021 pdf
  6. Nguyen Hien Duy, Nguyen TrungTin, Chamroukhi Faicel and McLachlan Geoffrey John. Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications SpringerOpen., Vol. 8(1), pages:1–15, 2021 pdf 
    @article{nguyen2020approxMoE,
 title = {Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models},
 author = {Nguyen Hien Duy and Nguyen TrungTin and Chamroukhi Faicel and McLachlan Geoffrey John},
 journal = {Journal of Statistical Distributions and Applications},
 volume = {8},
 number = {1},
 pages = {1--15},
 year = {2021},
 publisher = {SpringerOpen},
 url = {https://jsdajournal.springeropen.com/track/pdf/10.1186/s40488-021-00125-0.pdf},
}
  1. F. Chamroukhi. Statistical Analysis Tools for Complex Time-Series Data. Talk hosted by R-Ladies, Tunis Section., nov, 2020 pdf
  2. F. Chamroukhi. Towards a Random Ecosystem of Data Intelligence. Talk @ The Mathematical Sciences department Auckland University of Technology., 2020 pdf
  3. Nguyen, TrungTin, Nguyen, Hien D, Chamroukhi, Faicel and McLachlan, Geoffrey J. An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models. arXiv preprint arXiv:2009.10622, 2020 pdf 
    @article{nguyen2020l_1MoE,
 title = {An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models},
 author = {Nguyen, TrungTin and Nguyen, Hien D and Chamroukhi, Faicel and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2009.10622},
 url = {https://arxiv.org/pdf/2009.10622.pdf},
 year = {2020},
}
  4. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D and McLachlan, Geoffrey J. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. arXiv preprint arXiv:2008.09787, 2020 pdf 
    @article{nguyen2020approximation,
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien D and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2008.09787},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
 year = {2020},
}
  5. T Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi and Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics & Statistics Cogent OA., Vol. 7(1), pages:1750861, 2020 pdf 
    @article{approx-by-fmmm-2020,
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 author = {T Tin Nguyen and Hien D Nguyen and Faicel Chamroukhi and Geoffrey J McLachlan},
 journal = {Cogent Mathematics \& Statistics},
 volume = {7},
 number = {1},
 pages = {1750861},
 year = {2020},
 publisher = {Cogent OA},
 url = {https://www.tandfonline.com/doi/pdf/10.1080/25742558.2020.1750861},
}
  1. F. Chamroukhi. Mixtures-of-Experts for Functional Data. Talk @ The University of Queensland, School of Mathematics and Physics., August, 2019 pdf
  2. T. Huynh and F. Chamroukhi. Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models. arXiv:1810.12161, July, 2019 pdf 
    @article{prEMME-2019,
 author = {T. Huynh and F. Chamroukhi},
 title = {Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models},
 volume = {},
 month = {July},
 year = {2019},
 url = {https://chamroukhi.com/papers/prEMME.pdf},
 eprint = { arXiv:1810.12161},
}
  3. Chamroukhi, Faïcel, Lecocq, Florian and Nguyen, Hien D. Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models., pages:42–56, July, 2019, Research School on Statistics and Data Science. pdf 
    @inproceedings{chamroukhi2019rMOGGE,
 title = {Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models},
 author = {Chamroukhi, Fa{\"\i}cel and Lecocq, Florian and Nguyen, Hien D},
 booktitle = {Research School on Statistics and Data Science},
 pages = {42--56},
 year = {2019},
 month = {July},
 organization = {Springer},
 url = {https://chamroukhi.com/papers/HD-MoGGE_Submitted},
}
  4. F. Chamroukhi. Unsupervised Statistical Learning from Heterogeneous Time-Series and Functional Data. Keynote note talk at the Research School on Statistics and Data Science-RSSDS 2019., July, 2019 pdf
  5. F. Chamroukhi. Functional Mixtures-of-Experts. Working group on Statistics & Data Sciences @ LMNO., June, 2019 pdf
  6. F. Chamroukhi. Functional Mixtures-of-Experts., April, 2019, CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session. slides 
    @inproceedings{Chamroukhi-mda-2019,
 author = {F. Chamroukhi},
 title = {Functional Mixtures-of-Experts},
 booktitle = {CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session},
 address = {Limassol, Cyprius},
 year = {2019},
 month = {April},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CRONOS-MDA-2019.pdf},
}
  7. F. Chamroukhi. Functional Mixtures-of-Experts. Invited talk at the Model-based and multivariate functional data session, CRONOS Multivariate Data Analysis workshop., April, 2019 pdf
  8. Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. arXiv:1903.00147, March, 2019 pdf 
    @article{approx-by-fmmm-2019,
 author = {Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan},
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 volume = {},
 month = {March},
 year = {2019},
 url = {https://arxiv.org/pdf/1903.00147.pdf},
 eprint = {arXiv:1903.00147},
}
  9. Faicel Chamroukhi and Bao T. Huynh. Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models. Journal de la Société Française de Statistique, Vol. 160(1), pages:57–85, March, 2019 pdf 
    @article{Chamroukhi-RMoE-2018,
 journal = {Journal de la Soci\'et\'e Fran\c caise de Statistique},
 author = {Faicel Chamroukhi and Bao T. Huynh},
 title = {{Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models}},
 volume = {160},
 number = {1},
 pages = {57--85},
 month = {March},
 year = {2019},
 url = {https://chamroukhi.com/papers/Chamroukhi_Huynh_jsfds-published.pdf},
}
     
  10. F. Chamroukhi. Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Kickoff meeting of the project ANR SMILES., February, 2019 pdf
  11. Faicel Chamroukhi and Hien D. Nguyen. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Jan., 2019 pdf 
    @article{Chamroukhi-FDA-2019,
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 author = {Faicel Chamroukhi and Hien D. Nguyen},
 doi = {10.1002/widm.1298.},
 volume = {},
 title = {Model-Based Clustering and Classification of Functional Data},
 year = {2019},
 month = {Jan.},
 url = {https://chamroukhi.com/papers/MBCC-FDA.pdf},
}
          
  12. Hien D. Nguyen, Faicel Chamroukhi and Florence Forbes. Approximation results regarding the multiple-output mixture of linear experts model. Neurocomputing, Vol. doi:10.1016/j.neucom.2019.08.014, 2019 
    @article{Nguyen-Chamroukhi-Forbes-2019,
 journal = {Neurocomputing},
 author = {Hien D. Nguyen and Faicel Chamroukhi and Florence Forbes},
 title = {Approximation results regarding the multiple-output mixture of linear experts model},
 volume = {doi:10.1016/j.neucom.2019.08.014},
 year = {2019},
}
  1. F. Chamroukhi. Statistical data science and some unsupervised learning problems. Keynote note talk @ the International Symposium on Data Science and Computational Intelligence DSCI 2018., December, 2018 pdf
  2. F. Chamroukhi. ANR SMILES. Journée de l'ANR du 29 novembre 2018: Réunion de lancements de projets du CE 40., november, 2018 pdf
  3. F. Chamroukhi and B.T Huynh. Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering., July, 2018, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2018,
 author = {F. Chamroukhi and B.T Huynh},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = { },
 address = {Rio, Brazil},
 title = {Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering},
 year = {2018},
 month = {July},
 url = {https://chamroukhi.com/papers/FChamroukhi-IJCNN2018.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2018-Talk.pdf},
}
  4. F. Chamroukhi. Model-based (co-)clustering in some high-dimensional scenarios. Annual Summer Session of the Working Group on Model-Based Clustering, Ann Arbor, USA., July, 2018 pdf
  5. Chamroukhi, F. and Huynh, Bao Tuyen. Regularized Mixture of Experts for High-Dimensional Data., June, 2018, Actes des 50 èmes Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2018,
 author = {Chamroukhi, F. and Huynh, Bao Tuyen},
 title = {Regularized Mixture of Experts for
High-Dimensional Data},
 booktitle = {Actes des 50 {\`e}mes Journ{\'e}es de Statistique},
 pages = { },
 address = {Paris},
 month = {June},
 year = {2018},
 url = {https://chamroukhi.com/papers/Chamroukhi-Huynh-JdS2018.pdf},
}
  6. F. Chamroukhi. Introduction of S4D 2018. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  7. F. Chamroukhi. Unsupervised learning from high-dimensional and functional data. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  8. F. Chamroukhi. On some new Mixtures-of-Experts Models. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  9. B.T Huynh and F. Chamroukhi. Regularised maximum-likelihood inference of mixture of experts for regression and clustering., April, 2018, Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2018,
 author = {B.T Huynh and F. Chamroukhi},
 booktitle = {Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Regularised maximum-likelihood inference of mixture of experts for regression and clustering},
 year = {2018},
 month = {April},
 url = {https://chamroukhi.com/papers/ESANN2018.pdf},
}
  10. Hien D. Nguyen and Faicel Chamroukhi. Practical and theoretical aspects of mixture-of-experts modeling: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Wiley Periodicals, Inc., pages:e1246–n/a, Feb., 2018 pdf 
    @article{NguyenChamroukhi-MoE,
 author = {Hien D. Nguyen and Faicel Chamroukhi},
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 title = {Practical and theoretical aspects of mixture-of-experts modeling: An overview},
 publisher = {Wiley Periodicals, Inc},
 issn = {1942-4795},
 doi = {10.1002/widm.1246},
 pages = {e1246--n/a},
 keywords = {classification, clustering, mixture models, mixture of experts, neural networks},
 month = {Feb.},
 year = {2018},
 url = {https://chamroukhi.com/papers/Nguyen-Chamroukhi-MoE-DMKD-2018},
}
        
  11. F. Chamroukhi. Asterics: Apprentissage Statistique à l'Echelle pour la Représentation et la Classification non-supervisées. Normandie Université., 02 Oct, 2018 pdf
  12. F. Chamroukhi. SMILES: Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Talk at the days of the Normandy Federations of Mathematics (Normath) and Information Sciences and Technologies (Normastic)., 12 Oct, 2018 pdf
  13. V. Roger, M. Ferrari, R. Marxer, F. Chamroukhi and H. Glotin. Towards the topology of autoencoder of calls versus clicks of marine mammal. The Journal of the Acoustical Society of America, Vol. 144(3), pages:1777–1778, 2018 pdf 
    @article{roger2018,
 title = {Towards the topology of autoencoder of calls versus clicks of marine mammal},
 author = {V. Roger and M. Ferrari and R. Marxer and F. Chamroukhi and H. Glotin},
 journal = {The Journal of the Acoustical Society of America},
 volume = {144},
 number = {3},
 pages = {1777--1778},
 year = {2018},
 url = {http://asa.scitation.org/doi/10.1121/1.5067859},
}
  14. V. Roger, M. Bartcus, F. Chamroukhi and Hervé Glotin. Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model. Multimedia Tools and Applications for Environmental & Biodiversity Informatics, pages:113-130, 2018 pdf 
    @article{Roger-HDP-HMM-2018,
 journal = {Multimedia Tools and Applications for Environmental \& Biodiversity Informatics},
 author = {V. Roger and M. Bartcus and F. Chamroukhi and Herv\'e Glotin},
 title = {Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model},
 volume = {},
 pages = {113-130},
 year = {2018},
 url = {https://chamroukhi.com/papers/Roger-et-al-MTAP.pdf},
}
  15. F. Chamroukhi, M. Bartcus and H. Glotin. Dirichlet Process Parsimonious Gaussian Mixture for clustering. arXiv:1501.03347v2, 2018 pdf 
    @article{Chamroukhi-DPPM-2018,
 author = {F. Chamroukhi and M. Bartcus and H. Glotin},
 eprint = {arXiv:1501.03347v2},
 publisher = {},
 title = {Dirichlet Process Parsimonious Gaussian Mixture for clustering},
 volume = {},
 url = {https://chamroukhi.com/papers/DPPM.pdf},
 year = {2018},
 note = {submitted},
}
  1. F. Chamroukhi. Model-Based Co-Clustering of High-Dimensional Functional Data. ERCIM 2017 : The 10th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2017
  2. F. Chamroukhi. Skew $t$ mixture of experts. Neurocomputing - Elsevier, Vol. 266, pages:390-408, November, 2017 pdf 
    @article{Chamroukhi-STMoE-2017,
 author = {F. Chamroukhi},
 journal = {Neurocomputing - Elsevier},
 title = {Skew $t$ mixture of experts},
 volume = {266},
 pages = {390-408},
 year = {2017},
 month = {November},
 url = {https://chamroukhi.com/papers/STMoE.pdf},
}
        
  3. F. Chamroukhi and C. Biernacki. Model-Based Co-Clustering of Multivariate Functional Data., Jul, 2017, ISI 2017 - 61st World Statistics Congress. pdf 
    @inproceedings{Chamroukhi-ISI-2017,
 author = {F. Chamroukhi and C. Biernacki},
 booktitle = {ISI 2017 - 61st World Statistics Congress},
 title = {{Model-Based Co-Clustering of Multivariate Functional Data}},
 month = {Jul},
 year = {2017},
 address = {Marrakech, Morocco},
 url = {https://chamroukhi.com/papers/ISI2017.pdf},
}
  4. F. Chamroukhi. Introduction on Sata Science. International Research Summer School in Statistics & BigData Science - SBDS 2017., june, 2017 pdf
  5. F. Chamroukhi. Sciences des données. Journée Interlabs, Pôle de Compététivité Transactions Electroniques Securisées - TES., 27 Nov, 2017 pdf
  6. F. Chamroukhi. On some statistical data analysis and learning problems in Data Science. Plenary talk at the days of the Normandy Federation of Information Sciences and Technologies (Normastic), Rouen, France., 30 Mar, 2017 pdf
  7. F. Chamroukhi. On some mixtures for modeling complex datasets. Séminaire Statistique et Sciences des données, Laboratoire de Mathématiques Nicolas Oresme (UMR LMNO)., 02 feb, 2017 pdf
  1. F. Chamroukhi. Unsupervised learning of regression mixture models with unknown number of components. Journal of Statistical Computation and Simulation Taylor & Francis Online., Vol. 86, pages:2308–2334, Nov, 2016 pdf 
    @article{Chamroukhi-RobustEMMixReg2015,
 author = {F. Chamroukhi},
 journal = {Journal of Statistical Computation and Simulation},
 title = {Unsupervised learning of regression mixture models with unknown number of components},
 volume = {86},
 pages = {2308--2334},
 month = {Nov},
 year = {2016},
 publisher = {Taylor \& Francis Online},
 url = {https://chamroukhi.com/papers/Chamroukhi-JSCS-2015.pdf},
}
      
  2. F. Chamroukhi. Skew-Normal Mixture of Experts., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-SNMoE-IJCNN-2016,
 author = {F. Chamroukhi},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {Skew-Normal Mixture of Experts},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-SNMoE-IJCNN2016.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2016-Talk.pdf},
}
        
  3. R. Jaziri, F. Chamroukhi, M. Lebbah and Y. Bennani. GTM Mixture through time for sequential data., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2016,
 author = {R. Jaziri and F. Chamroukhi and M.  Lebbah and Y. Bennani},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {GTM Mixture through time for sequential data},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-GTMTT-IJCNN2016.pdf},
}
  4. F. Chamroukhi. Bayesian mixture of spatial spline regressions., April, 2016, Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-BSSRM-ESANN-2016,
 author = {F. Chamroukhi},
 booktitle = {Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian mixture of spatial spline regressions},
 year = {2016},
 month = {April},
 url = {https://chamroukhi.com/papers/Bayes-SSRM-ESANN-2016.pdf},
}
  5. F. Chamroukhi. Hierarchical dynamical mixtures for functional data clustering and segmentation. Joint Statistics Seminar, Department of mathematics, KU Leuven, Leuven, Belgium., 01 Dec, 2016 pdf
  6. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Plenary talk at the days of the Federation of Mathematics of Normandie (FNM), Rouen, France., 06 Oct, 2016 pdf
  7. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of Institut Elie Cartan, IECL - UMR CNRS 7502 - Nancy, France., April 18, 2016 pdf
  8. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of LORIA, UMR CNRS 7503 - Nancy, France., March 23, 2016 pdf
  9. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Evry - LaMME UMR CNRS 8071, Evry, France., March 17, 2016 pdf
  10. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Caen - LMNO UMR CNRS 6139, Caen, France., March 15, 2016 pdf
  11. F. Chamroukhi. Statistical learning of generative models for complex data analysis. Seminar of the lab of fundamental computer science of Marseille - LIF UMR CNRS 7279, Marseille, France., March 1st, 2016 pdf
  12. F. Chamroukhi. Statistical learning of generative models for signal processing. Seminar of the center of research in computer science, signal and automatic control of Lille, Signal, Models and Applications-SIGMA team., March 07, 2016 pdf
  13. F. Chamroukhi. On some mixtures for modeling complex datasets. Seminar of Inria Lille-Nord Europe, Models ofr Data Analysis and Learning (Modal) team., 12 jan, 2016 pdf
  14. F. Chamroukhi. Robust mixture of experts modeling using the $t$-distribution. Neural Networks - Elsevier, Vol. 79, pages:20–36, 2016 pdf 
    @article{Chamroukhi-TMoE-2016,
 author = {F. Chamroukhi},
 journal = {Neural Networks - Elsevier},
 title = {Robust mixture of experts modeling using the $t$-distribution},
 volume = {79},
 pages = {20--36},
 year = {2016},
 url = {https://chamroukhi.com/papers/TMoE.pdf},
}
        
  15. Faicel Chamroukhi. Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation. Journal of Classification, Vol. 33(3), pages:374–411, 2016 pdf 
    @article{Chamroukhi-PWRM-2016,
 author = {Faicel Chamroukhi},
 title = {Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation},
 journal = {Journal of Classification},
 volume = {33},
 number = {3},
 pages = {374--411},
 url = {https://chamroukhi.com/papers/Chamroukhi-PWRM-JournalClassif-2016.pdf},
 year = {2016},
}
  1. F. Chamroukhi. Robust non-normal mixtures of experts. ERCIM 2015 : The 8th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2015 pdf slides
  2. F. Chamroukhi. Bayesian mixtures of spatial spline regressions. arXiv:1508.00635, Aug, 2015 pdf 
    @article{Chamroukhi-BSSRM-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1508.00635},
 title = {Bayesian mixtures of spatial spline regressions},
 volume = {},
 url = {http://arxiv.org/pdf/1508.00635.pdf},
 month = {Aug},
 year = {2015},
}
  3. F. Chamroukhi. Non-Normal Mixtures of Experts. arXiv:1506.06707, July, 2015 pdf 
    @article{Chamroukhi-NNMoE-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1506.06707},
 title = {Non-Normal Mixtures of Experts},
 volume = {},
 url = {http://arxiv.org/pdf/1506.06707.pdf},
 month = {July},
 year = {2015},
 note = {Report (61 pages)},
}
        
  4. M. Bartcus, F. Chamroukhi and H. Glotin. Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis., July, 2015, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2015,
 author = {M. Bartcus and F. Chamroukhi and H.  Glotin},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = {},
 address = {Killarney, Ireland},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis},
 year = {2015},
 month = {July},
 url = {https://chamroukhi.com/papers/IJCNN2015.pdf},
}
  5. Chamroukhi, F.. Statistical learning of latent data models for complex data analysis. Habilitation Thesis (HDR), Université de Toulon, 07 december, 2015 pdf 
    @phdthesis{Chamroukhi-HDR-2015,
 author = {Chamroukhi, F.},
 title = {Statistical learning of latent data models for complex data analysis},
 school = {Universit\'e de Toulon},
 month = {07 december},
 year = {2015},
 type = {{Habilitation Thesis (HDR)}},
 url = {https://chamroukhi.com/FChamroukhi-HDR.pdf},
}
  6. F. Chamroukhi. Statistical learning of latent data models for complex data analysis. Seminar of the lab of Mathematics Paul Painlevé, UMR CNRS 8524., 04 Nov, 2015 pdf
  7. F. Attal, M. Dedabrishvili, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Physical human activity recognition using wearable sensors. Sensors, Vol. 15(12), pages:31314-31338, 2015 pdf 
    @article{Chamroukhi-Sensors-2015,
 author = {F. Attal and M. Dedabrishvili and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 journal = {Sensors},
 title = {Physical human activity recognition using wearable sensors},
 volume = {15},
 number = {12},
 url = {https://chamroukhi.com/papers/Sensors-2015.pdf},
 pages = {31314-31338},
 year = {2015},
}
  1. F. Chamroukhi. Model-based cluster and discriminant analysis for functional data. ERCIM 2014 : The 7th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, Pisa, Italy., December, 2014 pdf
  2. F. Chamroukhi, M. Bartcus and H. Glotin. Bayesian non-parametric parsimonious Gaussian mixture for clustering., August, 2014, Proceedings of 22nd International Conference on Pattern Recognition (ICPR). pdf 
    @inproceedings{Chamroukhi-ICPR-2014,
 author = {F. Chamroukhi and M. Bartcus and H.  Glotin},
 booktitle = {Proceedings of 22nd International Conference on Pattern Recognition (ICPR)},
 pages = {},
 address = {Stockholm},
 title = {Bayesian non-parametric parsimonious Gaussian mixture for clustering},
 year = {2014},
 month = {August},
 url = {https://chamroukhi.com/papers/IPGMM-ICPR-2014.pdf},
}
  3. M. Bartcus and F. Chamroukhi. Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data., June, 2014, Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML). pdf 
    @inproceedings{Chamroukhi-wkpICML-2014,
 author = {M. Bartcus and F. Chamroukhi},
 booktitle = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 pages = {},
 address = {Beijing},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data},
 year = {2014},
 month = {June},
 url = {https://chamroukhi.com/papers/ICMLwkshp_iHMM_2014.pdf},
}
  4. F. Chamroukhi. Mixture models for cluster analysis: from model-based inference to Bayesian non-parametrics. uLearnBio workshop of the International Conference on Machine Learning (ICML), Beijing., June, 2014 pdf
  5. F. Chamroukhi, Marius Bartcus and Herve Glotin. Bayesian non-parametric parsimonious clustering., April, 2014, Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2014,
 author = {F. Chamroukhi and Marius Bartcus and Herve  Glotin},
 booktitle = {Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian non-parametric parsimonious clustering},
 year = {2014},
 month = {April},
 url = {https://chamroukhi.com/papers/IPGMM-esann2014},
 slides = {https://chamroukhi.com/conf-presentations/Friday_12h05_Chamroukhi_esann2014},
}
  6. M. Bartcus, F. Chamroukhi and H. Glotin. Clustering Bayésien Parcimonieux Non-Paramétrique., pages:3–13, Jan, 2014, Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering. pdf 
    @inproceedings{Chamroukhi-EGC-2014,
 author = {M. Bartcus and F. Chamroukhi and H. Glotin},
 title = {Clustering Bay{\'e}sien Parcimonieux Non-Param{\'e}trique},
 booktitle = {Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering},
 pages = {3--13},
 address = {Rennes, France},
 month = {Jan},
 year = {2014},
 url = {https://chamroukhi.com/papers/IPGMM-EGC-CluCo-2014.pdf},
}
  7. Chamroukhi, F. and Glotin, H.. Unsupervised learning from big bioacoustic data (uLearnBio). Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)., 2014 
    @book{ProceedingsUlearnBioICML2014,
 author = {Chamroukhi, F. and Glotin, H.},
 title = {Unsupervised learning from big bioacoustic data (uLearnBio)},
 publisher = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 year = {2014},
 address = {Beijing, China},
 isbn = {979-10-90821-06-4},
 note = {\href{http://sabiod.univ-tln.fr/ulearnbio/}{home page}},
}
  1. F. Chamroukhi, D. Trabelsi, S. Mohammed, L. Oukhellou and Y. Amirat. Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing Elsevier., Vol. 120, pages:633–644, November, 2013 pdf 
    @article{Chamroukhi-MRHLP-2013,
 author = {F. Chamroukhi and D. Trabelsi and S. Mohammed and L. Oukhellou and Y. Amirat},
 journal = {Neurocomputing},
 month = {November},
 pages = {633--644},
 publisher = {Elsevier},
 title = {Joint segmentation of multivariate time series with hidden process regression for human activity recognition},
 volume = {120},
 year = {2013},
 note = {},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013b.pdf},
}
        
  2. F. Chamroukhi. Robust EM algorithm for model-based curve clustering., pages:1–8, August, 2013, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2013,
 author = {F. Chamroukhi},
 title = {Robust {EM} algorithm for model-based curve clustering},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Dallas, Texas},
 month = {August},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-Robust-EM-MixReg-IJCNN2013.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
}
      
  3. F. Chamroukhi, H. Glotin and A. Samé. Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing, Vol. 112, pages:153-163, July, 2013 pdf 
    @article{Chamroukhi-FMDA-neucomp2013,
 author = {F. Chamroukhi and H. Glotin and A. Sam{\'e}},
 journal = {Neurocomputing},
 pages = {153-163},
 title = {Model-based functional mixture discriminant analysis with hidden process regression for curve classification},
 volume = {112},
 year = {2013},
 month = {July},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013a.pdf},
}
          
  4. Marius Bartcus, Faicel Chamroukhi and Hervé Glotin. Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture., pages:205–211, 2013, Proceedings of the NIPS4B workshop, Neural Information Processing Systems (NIPS). pdf 
    @inproceedings{Chamroukhi-wkpNIPS-2013,
 author = {Marius Bartcus and Faicel Chamroukhi and Herv{\'e}  Glotin},
 title = {Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture},
 booktitle = {Proceedings of the NIPS4B workshop,  Neural Information Processing Systems (NIPS)},
 pages = {205--211},
 address = {Nevada, USA},
 year = {2013},
 url = {https://chamroukhi.com/papers/nips4b_wkshop.pdf},
}
  5. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression. IEEE Transactions on Automation Science and Engineering, Vol. 3(10), pages:829–335, 2013 pdf 
    @article{Chamroukhi-MHMMR-2013,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {IEEE Transactions on Automation Science and Engineering},
 number = {10},
 pages = {829--335},
 title = {An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression},
 volume = {3},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-MHMMR-IeeeTase.pdf},
}
        
  1. F. Chamroukhi. Learning probabilistic latent process models from temporal data. VIIth International Summer School ERMITES 2012 on Representations and Decisions in Cognitive Vision, La seyne/Mer., september, 2012 pdf
  2. F. Chamroukhi. Apprentissage de modèles probabilistes à processus latent à partir de données temporelles. Seminaire GFD-LIPADE Université Paris 5., july, 2012 pdf
  3. F. Chamroukhi and H. Glotin. Mixture model-based functional discriminant analysis for curve classification., pages:1–8, June, 2012, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2012,
 author = {F. Chamroukhi and H. Glotin},
 title = {Mixture model-based functional discriminant analysis for curve classification},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Brisbane, Australia},
 month = {June},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2012.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
 software = {https://github.com/fchamroukhi/MixRHLP_Matlab},
}
  4. F. Chamroukhi, H. Glotin and C. Rabouy. Functional Mixture Discriminant Analysis with hidden process regression for curve classification., pages:281–286, April, 2012, Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012,
 author = {F. Chamroukhi and H.  Glotin and C. Rabouy},
 booktitle = {Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {281--286},
 address = {Bruges, Belgium},
 title = {Functional Mixture Discriminant Analysis with hidden process regression for curve classification},
 year = {2012},
 month = {April},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  5. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Supervised and unsupervised classification approaches for human activity recognition using body-mounted sensors., pages:417–422, April, 2012, Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012-2,
 author = {D. Trabelsi  and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 booktitle = {Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {417--422},
 address = {Bruges, Belgium},
 title = {Supervised and unsupervised classification approaches for human activity recognition using
body-mounted sensors},
 month = {April},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012-2.pdf},
}
  6. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Feb, 2012 pdf
  7. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  8. F. Chamroukhi. Apprentissage de modèles probabilistes génératifs : Approches Bayésiennes Parcimonieuses pour des données fonctionnelles et des données multidimensionnelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  9. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. Classification automatique de données temporelles en classes ordonnées., Mai, 2012, Actes des 44 ème Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2012,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {Classification automatique de donn{\'e}es temporelles en classes ordonn{\'e}es},
 booktitle = {Actes des 44 {\`e}me Journ{\'e}es de Statistique},
 pages = { },
 address = {Bruxelles, Belgique},
 month = {Mai},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-jds-2012.pdf},
}
  10. Glotin, H., Chamroukhi, F. and Maillot, T.. Representations and Decisions in Cognitive Vision. Proceedings of the International Summer School ERMITES 2012., pages:507 pages, 2012 pdf 
    @book{ProceedingsErmites2012,
 author = {Glotin, H. and Chamroukhi,  F. and Maillot, T.},
 title = {Representations and Decisions in Cognitive Vision},
 publisher = {Proceedings of the International Summer School ERMITES 2012},
 year = {2012},
 pages = {507 pages},
 url = {http://sis.univ-tln.fr/~glotin/ERMITES12/Actes_ERMITES12.pdf},
 isbn = {979-10-90821-00-2},
}
  1. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Activity Recognition Using Hidden Markov Models., september, 2011, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 
    @inproceedings{Trabelsi-wkpIROS-2011,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 title = {Activity Recognition Using Hidden Markov Models},
 booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
 adress = {San Francisco, California},
 optpages = {},
 year = {2011},
 month = {september},
}
        
  2. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A dynamic probabilistic modeling of railway switches operating states., May, 2011, Proccedings of the 9th World Congress on Railway Research (WCRR). pdf 
    @inproceedings{Chamroukhi-WCRR-2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 title = {A dynamic probabilistic modeling of railway switches operating states},
 booktitle = {Proccedings of the 9th World Congress on Railway Research (WCRR)},
 address = {Lille},
 year = {2011},
 month = {May},
 url = {https://chamroukhi.com/papers/WCRR-2011-Chamroukhi.pdf},
}
  3. F. Chamroukhi. Diagnostic par analyse de données longitudinales. TISIC, Séminaire n 13, "Analyse de Données Longitudinales"., March, 2011 pdf
  4. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Modèle à processus latent et algorithme EM pour la régression non linéaire. Revue des Nouvelles Technologies de l'Information (RNTI), Vol. S1, pages:15–32, Jan, 2011 pdf 
    @article{chamroukhi_et_al_rnti2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Revue des Nouvelles Technologies de l'Information (RNTI)},
 month = {Jan},
 pages = {15--32},
 title = {Mod\`ele \`a processus latent et algorithme {EM} pour la r\'egression non lin\'eaire},
 volume = {S1},
 year = {2011},
 url = {https://chamroukhi.com/papers/chamroukhi_same_govaert_aknin_rnti.pdf},
}
        
  5. F. Chamroukhi, A. Samé, P. Aknin and G. Govaert. Model-based clustering with Hidden Markov Model regression for time series with regime changes., pages:2814–2821, Jul-Aug, 2011, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf 
    @inproceedings{Chamroukhi-IJCNN-2011,
 author = {F. Chamroukhi and A. Sam\'e  and P. Aknin and G. Govaert},
 title = {Model-based clustering with Hidden Markov Model regression for time series with regime changes},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {2814--2821},
 adress = {San Jose, California, USA},
 year = {2011},
 month = {Jul-Aug},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf},
}
        
  6. Glotin, H., Chamroukhi, F. and Paris, S.. Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. Décomposition Parcimonieuse, Contraction et Structuration des Scènes Multimodales Complexes. Proceedings of the International Summer School ERMITES 2011., pages:436 pages, 2011 pdf 
    @book{ProceedingsErmites2011,
 author = {Glotin, H. and Chamroukhi,  F. and Paris, S.},
 title = {Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. D\'ecomposition Parcimonieuse, Contraction et Structuration des Sc\`enes Multimodales Complexes},
 publisher = {Proceedings of the International Summer School ERMITES 2011},
 year = {2011},
 pages = {436 pages},
 url = {https://chamroukhi.com/books/ERMITES_2011.pdf},
 isbn = {978-29-52474-79-5},
}
  7. F. Chamroukhi. Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms. Lap Lambert Academic Publishing., 2011 
    @book{Chamroukhi_book2011,
 author = {F. Chamroukhi},
 title = {Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms},
 publisher = {Lap Lambert Academic Publishing},
 year = {2011},
 isbn = {978-3844311372},
}
  8. A. Samé, F. Chamroukhi, G. Govaert and P. Aknin. Model-based clustering and segmentation of time series with changes in regime. Advances in Data Analysis and Classification Springer Berlin / Heidelberg., Vol. 5, pages:301–321, 2011 pdf 
    @article{Chamroukhi-MixRHLP-2011,
 author = {A. Sam{\'e} and F. Chamroukhi and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 issue = {4},
 journal = {Advances in Data Analysis and Classification},
 pages = {301--321},
 publisher = {Springer Berlin / Heidelberg},
 title = {Model-based clustering and segmentation of time series with changes in regime},
 volume = {5},
 year = {2011},
 url = {https://chamroukhi.com/papers/adac-2011.pdf},
}
          
  1. F. Chamroukhi. Modèle probabiliste à base de processus latent pour la description et la classification de signaux. The Computer Science lab of Paris 13 University (LIPN)., April, 2010 pdf
  2. Chamroukhi, F.. Hidden process regression for curve modeling, classification and tracking. Ph.D. Thesis, Université de Technologie de Compiègne, 13 december, 2010 pdf 
    @phdthesis{Chamroukhi_PhD_2010,
 author = {Chamroukhi, F.},
 title = {Hidden process regression for curve modeling, classification and tracking},
 school = {Universit\'e de Technologie de Compi\`egne},
 month = {13 december},
 year = {2010},
 type = {Ph.D. Thesis},
 url = {https://chamroukhi.com/FChamroukhi-PhD.pdf},
}
  3. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing, Vol. 73(7-9), pages:1210–1221, 2010 pdf 
    @article{chamroukhi_et_al_neurocomp2010,
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Neurocomputing},
 number = {7-9},
 pages = {1210--1221},
 title = {A hidden process regression model for functional data description. Application to curve discrimination},
 volume = {73},
 year = {2010},
 url = {https://chamroukhi.com/papers/chamroukhi_neucomp_2010.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  1. F. Chamroukhi. Modélisation probabiliste à base de processus latent de signaux monodimensionnels. Business Intelligence lab, Télécom-ParisTech., October, 2009 pdf
  2. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2009 pdf
  3. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for feature extraction from time series., pages:489–496, June, 2009, International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2009,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {A regression model with a hidden logistic process for feature extraction from time series},
 booktitle = {International Joint Conference on Neural Networks (IJCNN)},
 year = {2009},
 month = {June},
 pages = {489--496},
 address = {Atlanta, GA},
 url = {https://chamroukhi.com/papers/chamroukhi_ijcnn2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_IJCNN2009},
}
          
  4. F. Chamroukhi. Modèle à processus latent pour la paramétrisation et l'apprentissage de signaux évolutifs. Application au diagnostic d'aiguillages. Séminaire DIAG, Inrets-LTN., April, 2009
  5. Samé, A., Chamroukhi, F. and Govaert, G.. Algorithme EM et modèle à processus latent pour la régression non linéaire., 2009, Actes des 41èmes Journées de Statistique de la SFDS. slides 
    @inproceedings{Chamroukhi-JDS-2009,
 author = {Sam\'e,  A. and Chamroukhi, F. and Govaert, G.},
 title = {Algorithme EM et mod\`ele \`a  processus latent pour la r\'egression non lin\'eaire},
 booktitle = {Actes des 41\`emes Journ\'ees de Statistique de la SFDS},
 pages = { },
 address = {Bordeaux},
 month = {},
 year = {2009},
 slides = {https://chamroukhi.com/conf-presentations/chamroukhi-slides-SFDS-2009.pdf},
}
          
  6. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Time series modeling by a regression approach based on a latent process. Neural Networks Elsevier Science Ltd.., Vol. 22(5-6), pages:593–602, 2009 pdf 
    @article{chamroukhi_et_al_NN2009,
 address = {Oxford, UK, UK},
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 journal = {Neural Networks},
 number = {5-6},
 pages = {593--602},
 publisher = {Elsevier Science Ltd.},
 title = {Time series modeling by a regression approach based on a latent process},
 volume = {22},
 year = {2009},
 url = {https://chamroukhi.com/papers/Chamroukhi_Neural_Networks_2009.pdf},
}
          
  7. Chamroukhi, F., Samé, A. and Aknin, P.. A probabilistic approach for the classification of railway switch operating states. IEEE., 2009, The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. 
    @inproceedings{Chamroukhi-RCM-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Aknin, P.},
 booktitle = {The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies},
 title = {A probabilistic approach for the classification of railway switch operating states},
 year = {2009},
 address = {Dublin, UK},
 publisher = {IEEE},
}
  8. Onanena, R., Chamroukhi, F., Oukhellou, L., Candusso, D., Aknin, P. and Hissel, D.. Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context. IEEE Computer Society., pages:632–637, 2009, Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA). pdf slides 
    @inproceedings{Chamroukhi-ICMLA-2009,
 author = {Onanena, R. and Chamroukhi, F. and Oukhellou, L. and Candusso, D. and Aknin, P. and Hissel, D.},
 title = {Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context},
 booktitle = {Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)},
 year = {2009},
 pages = {632--637},
 publisher = {IEEE Computer Society},
 address = {Miami, Flordia, USA},
 url = {https://chamroukhi.com/papers/IEEE-ICMLA2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ICMLA2009.pdf},
}
  9. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for signal parameterization., pages:503-508, 2009, Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Govaert, G. and Aknin, P.},
 booktitle = {Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {503-508},
 title = {A regression model with a hidden logistic process for signal parameterization},
 year = {2009},
 url = {https://chamroukhi.com/papers/chamroukhi_esann_2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ESANN2009.pdf},
}
          
  1. F. Chamroukhi. Modele de regression a processus logistique cache pour le diagnostic par suivi de point de fonctionnement des m�canismes d'aiguillages. Journée des doctorants LTN, INRETS., Nov, 2008 pdf
  2. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2008 pdf
  3. F. Chamroukhi. Régression à variable latente pour la modélisation des signaux de manøevre d'aiguillages. Journée des doctorants SPI, INRETS., July, 2008
  4. Chamroukhi, F., Samé, A., Aknin, P. and Antoni, M.. Switch mechanism diagnosis using a pattern recognition approach. IEEE., pages:1–4, June, 2008, The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE). pdf slides 
    @inproceedings{Chamroukhi-RCM-2008,
 author = {Chamroukhi, F. and Sam\'e,  A. and Aknin, P. and Antoni, M.},
 title = {Switch mechanism diagnosis using a pattern recognition approach},
 booktitle = {The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE)},
 year = {2008},
 address = {Derby, UK},
 month = {June},
 pages = {1--4},
 issn = {0537-9989},
 publisher = {IEEE},
 url = {https://chamroukhi.com/papers/chamroukhi_rcm_2008.pdf},
 slides = {https://chamroukhi.com/papers/presentation_rcm_2008.pdf},
}
  5. A. Samé, F. Chamroukhi and P. Aknin. Détection séquentielle de défauts sur des signaux de manøeuvres d'aiguillage., June, 2008, Workshop Surveillance, Sûreté et Sécurité des Grands Systèmes 3SGS'08. 
    @inproceedings{Chamroukhi-3SGS-2008,
 author = {A. Sam\'e and F. Chamroukhi and P. Aknin},
 title = {D\'etection s\'equentielle de d\'efauts sur des signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Workshop Surveillance, S\^uret\'e et S\'ecurit\'e des Grands Syst\`emes 3SGS'08},
 address = {Troyes, France},
 month = {June},
 year = {2008},
}
  6. F. Chamroukhi, A. Samé and P. Aknin. Régression à variable latente pour la modélisation de signaux de manøeuvres d'aiguillage., Vol. 112, pages:57–64, 2008, Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus sûrs, plus efficaces et plus attractifs. 
    @inproceedings{Chamroukhi-JD-INRETS-2008,
 author = {F. Chamroukhi and A. Sam\'e and P. Aknin},
 title = {R\'egression \`a variable latente pour la mod\'elisation de signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus s\^urs, plus efficaces et plus attractifs},
 volume = {112},
 pages = {57--64},
 editor = {In J. Marais and M.~Berbineau, editors},
 year = {2008},
}
  1. Chamroukhi, F. and Glotin, H.. Unsupervised learning from big bioacoustic data (uLearnBio). Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)., 2014 
    @book{ProceedingsUlearnBioICML2014,
 author = {Chamroukhi, F. and Glotin, H.},
 title = {Unsupervised learning from big bioacoustic data (uLearnBio)},
 publisher = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 year = {2014},
 address = {Beijing, China},
 isbn = {979-10-90821-06-4},
 note = {\href{http://sabiod.univ-tln.fr/ulearnbio/}{home page}},
}
  2. Glotin, H., Chamroukhi, F. and Maillot, T.. Representations and Decisions in Cognitive Vision. Proceedings of the International Summer School ERMITES 2012., pages:507 pages, 2012 pdf 
    @book{ProceedingsErmites2012,
 author = {Glotin, H. and Chamroukhi,  F. and Maillot, T.},
 title = {Representations and Decisions in Cognitive Vision},
 publisher = {Proceedings of the International Summer School ERMITES 2012},
 year = {2012},
 pages = {507 pages},
 url = {http://sis.univ-tln.fr/~glotin/ERMITES12/Actes_ERMITES12.pdf},
 isbn = {979-10-90821-00-2},
}
  3. Glotin, H., Chamroukhi, F. and Paris, S.. Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. Décomposition Parcimonieuse, Contraction et Structuration des Scènes Multimodales Complexes. Proceedings of the International Summer School ERMITES 2011., pages:436 pages, 2011 pdf 
    @book{ProceedingsErmites2011,
 author = {Glotin, H. and Chamroukhi,  F. and Paris, S.},
 title = {Parsimonious Decomposition, Contraction and Structuration of Complex Multimodal Scenes. D\'ecomposition Parcimonieuse, Contraction et Structuration des Sc\`enes Multimodales Complexes},
 publisher = {Proceedings of the International Summer School ERMITES 2011},
 year = {2011},
 pages = {436 pages},
 url = {https://chamroukhi.com/books/ERMITES_2011.pdf},
 isbn = {978-29-52474-79-5},
}
  4. F. Chamroukhi. Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms. Lap Lambert Academic Publishing., 2011 
    @book{Chamroukhi_book2011,
 author = {F. Chamroukhi},
 title = {Probabilistic Learning From Longitudinal Data: Background, Novel theoretical models, Classifiers and Algorithms},
 publisher = {Lap Lambert Academic Publishing},
 year = {2011},
 isbn = {978-3844311372},
}
  1. Faïcel Chamroukhi, Nhat Thien Pham, Van Ha Hoang and Geoffrey J. McLachlan. Functional mixtures-of-experts. Statistics and Computing Springer., Vol. 34(3), pages:98, 2024 pdf 
    @article{chamroukhi2024iFME,
 title = {Functional mixtures-of-experts},
 author = {Fa{\"\i}cel Chamroukhi and Nhat Thien Pham and Van Ha Hoang and Geoffrey J. McLachlan},
 journal = {Statistics and Computing},
 volume = {34},
 number = {3},
 pages = {98},
 year = {2024},
 publisher = {Springer},
 url = {https://link.springer.com/article/10.1007/s11222-023-10379-0},
}
  2. TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods Taylor & Francis., Vol. 52(14), pages:5048-5059, 2023 pdf 
    @article{nguyen2023approximation,
 author = {TrungTin Nguyen, Faicel Chamroukhi, Hien D. Nguyen and Geoffrey J. McLachlan},
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 journal = {Communications in Statistics - Theory and Methods},
 volume = {52},
 number = {14},
 pages = {5048-5059},
 year = {2023},
 publisher = {Taylor \& Francis},
 doi = {10.1080/03610926.2021.2002360},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
}
  3. TrungTin Nguyen, Hien Duy Nguyen, Faicel Chamroukhi and Florence Forbes. A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models. Electronic Journal of Statistics Institute of Mathematical Statistics and Bernoulli Society., Vol. 16(2), pages:4742–4822, 2022 pdf 
    @article{nguyen2022non-asympt-MoE,
 title = {A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models},
 author = {TrungTin Nguyen and Hien Duy Nguyen and Faicel Chamroukhi and Florence Forbes},
 journal = {Electronic Journal of Statistics},
 volume = {16},
 number = {2},
 pages = {4742--4822},
 year = {2022},
 url = {https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/A-non-asymptotic-approach-for-model-selection-via-penalization-in/10.1214/22-EJS2057.full},
 publisher = {Institute of Mathematical Statistics and Bernoulli Society},
}
  4. Faicel Chamroukhi, Segolene Brivet, Peter Savadjiev, Mark Coates and Forghani, Reza. DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation. Diagnostics, Vol. 12(12), pages:3072, 2022 pdf 
    @article{chamroukhi2022dect-clust,
 title = {DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation},
 author = {Faicel Chamroukhi and Segolene Brivet and Peter Savadjiev and  Mark Coates and Forghani, Reza},
 journal = {Diagnostics},
 volume = {12},
 number = {12},
 pages = {3072},
 year = {2022},
 url = {https://www.mdpi.com/2075-4418/12/12/3072},
}
      
  5. Nguyen Hien Duy, Nguyen TrungTin, Chamroukhi Faicel and McLachlan Geoffrey John. Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications SpringerOpen., Vol. 8(1), pages:1–15, 2021 pdf 
    @article{nguyen2020approxMoE,
 title = {Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models},
 author = {Nguyen Hien Duy and Nguyen TrungTin and Chamroukhi Faicel and McLachlan Geoffrey John},
 journal = {Journal of Statistical Distributions and Applications},
 volume = {8},
 number = {1},
 pages = {1--15},
 year = {2021},
 publisher = {SpringerOpen},
 url = {https://jsdajournal.springeropen.com/track/pdf/10.1186/s40488-021-00125-0.pdf},
}
  6. T Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi and Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics & Statistics Cogent OA., Vol. 7(1), pages:1750861, 2020 pdf 
    @article{approx-by-fmmm-2020,
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 author = {T Tin Nguyen and Hien D Nguyen and Faicel Chamroukhi and Geoffrey J McLachlan},
 journal = {Cogent Mathematics \& Statistics},
 volume = {7},
 number = {1},
 pages = {1750861},
 year = {2020},
 publisher = {Cogent OA},
 url = {https://www.tandfonline.com/doi/pdf/10.1080/25742558.2020.1750861},
}
  7. Faicel Chamroukhi and Bao T. Huynh. Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models. Journal de la Société Française de Statistique, Vol. 160(1), pages:57–85, March, 2019 pdf 
    @article{Chamroukhi-RMoE-2018,
 journal = {Journal de la Soci\'et\'e Fran\c caise de Statistique},
 author = {Faicel Chamroukhi and Bao T. Huynh},
 title = {{Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models}},
 volume = {160},
 number = {1},
 pages = {57--85},
 month = {March},
 year = {2019},
 url = {https://chamroukhi.com/papers/Chamroukhi_Huynh_jsfds-published.pdf},
}
     
  8. Faicel Chamroukhi and Hien D. Nguyen. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Jan., 2019 pdf 
    @article{Chamroukhi-FDA-2019,
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 author = {Faicel Chamroukhi and Hien D. Nguyen},
 doi = {10.1002/widm.1298.},
 volume = {},
 title = {Model-Based Clustering and Classification of Functional Data},
 year = {2019},
 month = {Jan.},
 url = {https://chamroukhi.com/papers/MBCC-FDA.pdf},
}
          
  9. Hien D. Nguyen, Faicel Chamroukhi and Florence Forbes. Approximation results regarding the multiple-output mixture of linear experts model. Neurocomputing, Vol. doi:10.1016/j.neucom.2019.08.014, 2019 
    @article{Nguyen-Chamroukhi-Forbes-2019,
 journal = {Neurocomputing},
 author = {Hien D. Nguyen and Faicel Chamroukhi and Florence Forbes},
 title = {Approximation results regarding the multiple-output mixture of linear experts model},
 volume = {doi:10.1016/j.neucom.2019.08.014},
 year = {2019},
}
  10. Hien D. Nguyen and Faicel Chamroukhi. Practical and theoretical aspects of mixture-of-experts modeling: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Wiley Periodicals, Inc., pages:e1246–n/a, Feb., 2018 pdf 
    @article{NguyenChamroukhi-MoE,
 author = {Hien D. Nguyen and Faicel Chamroukhi},
 journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
 title = {Practical and theoretical aspects of mixture-of-experts modeling: An overview},
 publisher = {Wiley Periodicals, Inc},
 issn = {1942-4795},
 doi = {10.1002/widm.1246},
 pages = {e1246--n/a},
 keywords = {classification, clustering, mixture models, mixture of experts, neural networks},
 month = {Feb.},
 year = {2018},
 url = {https://chamroukhi.com/papers/Nguyen-Chamroukhi-MoE-DMKD-2018},
}
        
  11. V. Roger, M. Ferrari, R. Marxer, F. Chamroukhi and H. Glotin. Towards the topology of autoencoder of calls versus clicks of marine mammal. The Journal of the Acoustical Society of America, Vol. 144(3), pages:1777–1778, 2018 pdf 
    @article{roger2018,
 title = {Towards the topology of autoencoder of calls versus clicks of marine mammal},
 author = {V. Roger and M. Ferrari and R. Marxer and F. Chamroukhi and H. Glotin},
 journal = {The Journal of the Acoustical Society of America},
 volume = {144},
 number = {3},
 pages = {1777--1778},
 year = {2018},
 url = {http://asa.scitation.org/doi/10.1121/1.5067859},
}
  12. V. Roger, M. Bartcus, F. Chamroukhi and Hervé Glotin. Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model. Multimedia Tools and Applications for Environmental & Biodiversity Informatics, pages:113-130, 2018 pdf 
    @article{Roger-HDP-HMM-2018,
 journal = {Multimedia Tools and Applications for Environmental \& Biodiversity Informatics},
 author = {V. Roger and M. Bartcus and F. Chamroukhi and Herv\'e Glotin},
 title = {Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model},
 volume = {},
 pages = {113-130},
 year = {2018},
 url = {https://chamroukhi.com/papers/Roger-et-al-MTAP.pdf},
}
  13. F. Chamroukhi. Skew $t$ mixture of experts. Neurocomputing - Elsevier, Vol. 266, pages:390-408, November, 2017 pdf 
    @article{Chamroukhi-STMoE-2017,
 author = {F. Chamroukhi},
 journal = {Neurocomputing - Elsevier},
 title = {Skew $t$ mixture of experts},
 volume = {266},
 pages = {390-408},
 year = {2017},
 month = {November},
 url = {https://chamroukhi.com/papers/STMoE.pdf},
}
        
  14. F. Chamroukhi. Unsupervised learning of regression mixture models with unknown number of components. Journal of Statistical Computation and Simulation Taylor & Francis Online., Vol. 86, pages:2308–2334, Nov, 2016 pdf 
    @article{Chamroukhi-RobustEMMixReg2015,
 author = {F. Chamroukhi},
 journal = {Journal of Statistical Computation and Simulation},
 title = {Unsupervised learning of regression mixture models with unknown number of components},
 volume = {86},
 pages = {2308--2334},
 month = {Nov},
 year = {2016},
 publisher = {Taylor \& Francis Online},
 url = {https://chamroukhi.com/papers/Chamroukhi-JSCS-2015.pdf},
}
      
  15. F. Chamroukhi. Robust mixture of experts modeling using the $t$-distribution. Neural Networks - Elsevier, Vol. 79, pages:20–36, 2016 pdf 
    @article{Chamroukhi-TMoE-2016,
 author = {F. Chamroukhi},
 journal = {Neural Networks - Elsevier},
 title = {Robust mixture of experts modeling using the $t$-distribution},
 volume = {79},
 pages = {20--36},
 year = {2016},
 url = {https://chamroukhi.com/papers/TMoE.pdf},
}
        
  16. Faicel Chamroukhi. Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation. Journal of Classification, Vol. 33(3), pages:374–411, 2016 pdf 
    @article{Chamroukhi-PWRM-2016,
 author = {Faicel Chamroukhi},
 title = {Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation},
 journal = {Journal of Classification},
 volume = {33},
 number = {3},
 pages = {374--411},
 url = {https://chamroukhi.com/papers/Chamroukhi-PWRM-JournalClassif-2016.pdf},
 year = {2016},
}
  17. F. Attal, M. Dedabrishvili, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Physical human activity recognition using wearable sensors. Sensors, Vol. 15(12), pages:31314-31338, 2015 pdf 
    @article{Chamroukhi-Sensors-2015,
 author = {F. Attal and M. Dedabrishvili and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 journal = {Sensors},
 title = {Physical human activity recognition using wearable sensors},
 volume = {15},
 number = {12},
 url = {https://chamroukhi.com/papers/Sensors-2015.pdf},
 pages = {31314-31338},
 year = {2015},
}
  18. F. Chamroukhi, D. Trabelsi, S. Mohammed, L. Oukhellou and Y. Amirat. Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing Elsevier., Vol. 120, pages:633–644, November, 2013 pdf 
    @article{Chamroukhi-MRHLP-2013,
 author = {F. Chamroukhi and D. Trabelsi and S. Mohammed and L. Oukhellou and Y. Amirat},
 journal = {Neurocomputing},
 month = {November},
 pages = {633--644},
 publisher = {Elsevier},
 title = {Joint segmentation of multivariate time series with hidden process regression for human activity recognition},
 volume = {120},
 year = {2013},
 note = {},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013b.pdf},
}
        
  19. F. Chamroukhi, H. Glotin and A. Samé. Model-based functional mixture discriminant analysis with hidden process regression for curve classification. Neurocomputing, Vol. 112, pages:153-163, July, 2013 pdf 
    @article{Chamroukhi-FMDA-neucomp2013,
 author = {F. Chamroukhi and H. Glotin and A. Sam{\'e}},
 journal = {Neurocomputing},
 pages = {153-163},
 title = {Model-based functional mixture discriminant analysis with hidden process regression for curve classification},
 volume = {112},
 year = {2013},
 month = {July},
 url = {https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013a.pdf},
}
          
  20. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression. IEEE Transactions on Automation Science and Engineering, Vol. 3(10), pages:829–335, 2013 pdf 
    @article{Chamroukhi-MHMMR-2013,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {IEEE Transactions on Automation Science and Engineering},
 number = {10},
 pages = {829--335},
 title = {An unsupervised approach for automatic activity recognition based on Hidden Markov Model Regression},
 volume = {3},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-MHMMR-IeeeTase.pdf},
}
        
  21. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Modèle à processus latent et algorithme EM pour la régression non linéaire. Revue des Nouvelles Technologies de l'Information (RNTI), Vol. S1, pages:15–32, Jan, 2011 pdf 
    @article{chamroukhi_et_al_rnti2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Revue des Nouvelles Technologies de l'Information (RNTI)},
 month = {Jan},
 pages = {15--32},
 title = {Mod\`ele \`a processus latent et algorithme {EM} pour la r\'egression non lin\'eaire},
 volume = {S1},
 year = {2011},
 url = {https://chamroukhi.com/papers/chamroukhi_same_govaert_aknin_rnti.pdf},
}
        
  22. A. Samé, F. Chamroukhi, G. Govaert and P. Aknin. Model-based clustering and segmentation of time series with changes in regime. Advances in Data Analysis and Classification Springer Berlin / Heidelberg., Vol. 5, pages:301–321, 2011 pdf 
    @article{Chamroukhi-MixRHLP-2011,
 author = {A. Sam{\'e} and F. Chamroukhi and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 issue = {4},
 journal = {Advances in Data Analysis and Classification},
 pages = {301--321},
 publisher = {Springer Berlin / Heidelberg},
 title = {Model-based clustering and segmentation of time series with changes in regime},
 volume = {5},
 year = {2011},
 url = {https://chamroukhi.com/papers/adac-2011.pdf},
}
          
  23. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A hidden process regression model for functional data description. Application to curve discrimination. Neurocomputing, Vol. 73(7-9), pages:1210–1221, 2010 pdf 
    @article{chamroukhi_et_al_neurocomp2010,
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 date-added = {2014-10-22 20:08:41 +0000},
 date-modified = {2014-10-22 20:08:41 +0000},
 journal = {Neurocomputing},
 number = {7-9},
 pages = {1210--1221},
 title = {A hidden process regression model for functional data description. Application to curve discrimination},
 volume = {73},
 year = {2010},
 url = {https://chamroukhi.com/papers/chamroukhi_neucomp_2010.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  24. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. Time series modeling by a regression approach based on a latent process. Neural Networks Elsevier Science Ltd.., Vol. 22(5-6), pages:593–602, 2009 pdf 
    @article{chamroukhi_et_al_NN2009,
 address = {Oxford, UK, UK},
 author = {F. Chamroukhi and A. Sam\'{e} and G. Govaert and P. Aknin},
 journal = {Neural Networks},
 number = {5-6},
 pages = {593--602},
 publisher = {Elsevier Science Ltd.},
 title = {Time series modeling by a regression approach based on a latent process},
 volume = {22},
 year = {2009},
 url = {https://chamroukhi.com/papers/Chamroukhi_Neural_Networks_2009.pdf},
}
          
  1. Nguyen, TrungTin and Nguyen, Dung Ngoc and Nguyen, Hien Duy and Chamroukhi, Faicel. A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection. Springer Nature Singapore., pages:234–245, 2024, AI 2023: Advances in Artificial Intelligence. pdf 
    @inproceedings{Tin-ACML2023,
 author = {Nguyen, TrungTin
and Nguyen, Dung Ngoc
and Nguyen, Hien Duy
and Chamroukhi, Faicel},
 editor = {Liu, Tongliang
and Webb, Geoff
and Yue, Lin
and Wang, Dadong},
 title = {A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection},
 booktitle = {AI 2023: Advances in Artificial Intelligence},
 year = {2024},
 publisher = {Springer Nature Singapore},
 address = {Singapore},
 pages = {234--245},
 abstract = {We are motivated by the problem of identifying potentially nonlinear regression relationships between high-dimensional outputs and high-dimensional inputs of heterogeneous data. This requires regression, clustering, and model selection, simultaneously. In this framework, we apply the mixture of experts models which are among the most popular ensemble learning techniques developed in the field of neural networks. In particular, we consider a more general case of mixture of experts models characterized by multiple Gaussian experts whose means are polynomials of the input variables and whose covariance matrices have block-diagonal structures. More especially, each expert is weighted by a gating network that is a softmax function of a polynomial of the input variables. These models require several hyper-parameters, including the number of mixture components, the complexity of the softmax gating networks and Gaussian mean experts, and the hidden block-diagonal structures of the covariance matrices. We provide a non-asymptotic theory for model selection of such complex hyper-parameters using the slope heuristic approach in a penalized maximum likelihood estimation framework. Specifically, we establish a non-asymptotic risk bound on the penalized maximum likelihood estimation, which takes the form of an oracle inequality, given lower bound assumptions on the penalty function.},
 isbn = {978-981-99-8391-9},
 url = {https://hal.science/hal-03984011v3/document},
}
  2. Faïcel Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf slides 
    @inproceedings{Chamroukhi-CMStatistics2023,
 title = {Learning mixtures-of-experts from heterogenous and high-dimensional data},
 author = {Fa{\"\i}cel Chamroukhi},
 howpublished = {CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics},
 address = {Berlin (DE)},
 year = {2023},
 month = {December},
 url = {https://hal.science/hal-04348869},
 slides = {./Talks/FChamroukhi-Talk-CMStatistics-17122023.pdf},
}
  3. Abdelmouaiz Tebjou, Goran Frehse and Faïcel Chamroukhi. Data-driven Reachability using Christoffel Functions and Conformal Prediction., Vol. 204, pages:194–213, Sep, 2023, The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023). pdf 
    @inproceedings{tebjou-et-al-copa2023,
 title = {{Data-driven Reachability using Christoffel Functions and Conformal Prediction}},
 author = {Abdelmouaiz Tebjou and Goran Frehse and Fa{\"i}cel Chamroukhi},
 url = {https://proceedings.mlr.press/v204/tebjou23a/tebjou23a.pdf},
 booktitle = {{The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023)}},
 address = {Limassol, Cyprus},
 volume = {204},
 pages = {194--213},
 year = {2023},
 month = {Sep},
 organization = {Proceedings of Machine Learning Research},
}
  4. Brivet Ségolène, Chamroukhi Faïcel, Coates Mark, Forghani Reza and Savadjiev Peter. Model-based clustering of Dual-Energy CT images for tumor detection. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 
    @inproceedings{chamroukhi2021fME-image,
 title = {Model-based clustering of Dual-Energy CT images for tumor detection},
 author = {Brivet S{\'e}gol{\`e}ne and Chamroukhi Fa{\"\i}cel and Coates Mark and Forghani Reza  and
Savadjiev Peter},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
}
      
  5. Faicel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Mixtures-of-experts with functional predictors. The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 slides 
    @inproceedings{chamroukhi2021fME,
 title = {Mixtures-of-experts with functional predictors},
 author = {Faicel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 howpublished = {The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)},
 address = {London, UK},
 year = {2021},
 month = {December},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CMStatistics--19-12-2021.pdf},
}
  6. Chamroukhi, Faïcel, Lecocq, Florian and Nguyen, Hien D. Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models., pages:42–56, July, 2019, Research School on Statistics and Data Science. pdf 
    @inproceedings{chamroukhi2019rMOGGE,
 title = {Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models},
 author = {Chamroukhi, Fa{\"\i}cel and Lecocq, Florian and Nguyen, Hien D},
 booktitle = {Research School on Statistics and Data Science},
 pages = {42--56},
 year = {2019},
 month = {July},
 organization = {Springer},
 url = {https://chamroukhi.com/papers/HD-MoGGE_Submitted},
}
  7. F. Chamroukhi. Functional Mixtures-of-Experts., April, 2019, CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session. slides 
    @inproceedings{Chamroukhi-mda-2019,
 author = {F. Chamroukhi},
 title = {Functional Mixtures-of-Experts},
 booktitle = {CRONOS Multivariate Data Analysis workshop. Model-based and multivariate functional data Session},
 address = {Limassol, Cyprius},
 year = {2019},
 month = {April},
 slides = {https://chamroukhi.com/Talks/FChamroukhi-CRONOS-MDA-2019.pdf},
}
  8. F. Chamroukhi and B.T Huynh. Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering., July, 2018, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2018,
 author = {F. Chamroukhi and B.T Huynh},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = { },
 address = {Rio, Brazil},
 title = {Regularized Maximum-Likelihood Estimation of Mixture-of-Experts for Regression and Clustering},
 year = {2018},
 month = {July},
 url = {https://chamroukhi.com/papers/FChamroukhi-IJCNN2018.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2018-Talk.pdf},
}
  9. Chamroukhi, F. and Huynh, Bao Tuyen. Regularized Mixture of Experts for High-Dimensional Data., June, 2018, Actes des 50 èmes Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2018,
 author = {Chamroukhi, F. and Huynh, Bao Tuyen},
 title = {Regularized Mixture of Experts for
High-Dimensional Data},
 booktitle = {Actes des 50 {\`e}mes Journ{\'e}es de Statistique},
 pages = { },
 address = {Paris},
 month = {June},
 year = {2018},
 url = {https://chamroukhi.com/papers/Chamroukhi-Huynh-JdS2018.pdf},
}
  10. B.T Huynh and F. Chamroukhi. Regularised maximum-likelihood inference of mixture of experts for regression and clustering., April, 2018, Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2018,
 author = {B.T Huynh and F. Chamroukhi},
 booktitle = {Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Regularised maximum-likelihood inference of mixture of experts for regression and clustering},
 year = {2018},
 month = {April},
 url = {https://chamroukhi.com/papers/ESANN2018.pdf},
}
  11. F. Chamroukhi and C. Biernacki. Model-Based Co-Clustering of Multivariate Functional Data., Jul, 2017, ISI 2017 - 61st World Statistics Congress. pdf 
    @inproceedings{Chamroukhi-ISI-2017,
 author = {F. Chamroukhi and C. Biernacki},
 booktitle = {ISI 2017 - 61st World Statistics Congress},
 title = {{Model-Based Co-Clustering of Multivariate Functional Data}},
 month = {Jul},
 year = {2017},
 address = {Marrakech, Morocco},
 url = {https://chamroukhi.com/papers/ISI2017.pdf},
}
  12. R. Jaziri, F. Chamroukhi, M. Lebbah and Y. Bennani. GTM Mixture through time for sequential data., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2016,
 author = {R. Jaziri and F. Chamroukhi and M.  Lebbah and Y. Bennani},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {GTM Mixture through time for sequential data},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-GTMTT-IJCNN2016.pdf},
}
  13. F. Chamroukhi. Skew-Normal Mixture of Experts., July, 2016, The International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-SNMoE-IJCNN-2016,
 author = {F. Chamroukhi},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 address = {Vancouver, Canada},
 title = {Skew-Normal Mixture of Experts},
 year = {2016},
 month = {July},
 url = {https://chamroukhi.com/papers/Chamroukhi-SNMoE-IJCNN2016.pdf},
 slides = {https://chamroukhi.com/conf-presentations/FChamroukhi-IJCNN-2016-Talk.pdf},
}
        
  14. F. Chamroukhi. Bayesian mixture of spatial spline regressions., April, 2016, Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-BSSRM-ESANN-2016,
 author = {F. Chamroukhi},
 booktitle = {Proceedings of 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian mixture of spatial spline regressions},
 year = {2016},
 month = {April},
 url = {https://chamroukhi.com/papers/Bayes-SSRM-ESANN-2016.pdf},
}
  15. M. Bartcus, F. Chamroukhi and H. Glotin. Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis., July, 2015, The International Joint Conference on Neural Networks (IJCNN). pdf 
    @inproceedings{Chamroukhi-IJCNN-2015,
 author = {M. Bartcus and F. Chamroukhi and H.  Glotin},
 booktitle = {The International Joint Conference on Neural Networks (IJCNN)},
 pages = {},
 address = {Killarney, Ireland},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for Unsupervised Bioacoustic Analysis},
 year = {2015},
 month = {July},
 url = {https://chamroukhi.com/papers/IJCNN2015.pdf},
}
  16. F. Chamroukhi, M. Bartcus and H. Glotin. Bayesian non-parametric parsimonious Gaussian mixture for clustering., August, 2014, Proceedings of 22nd International Conference on Pattern Recognition (ICPR). pdf 
    @inproceedings{Chamroukhi-ICPR-2014,
 author = {F. Chamroukhi and M. Bartcus and H.  Glotin},
 booktitle = {Proceedings of 22nd International Conference on Pattern Recognition (ICPR)},
 pages = {},
 address = {Stockholm},
 title = {Bayesian non-parametric parsimonious Gaussian mixture for clustering},
 year = {2014},
 month = {August},
 url = {https://chamroukhi.com/papers/IPGMM-ICPR-2014.pdf},
}
  17. M. Bartcus and F. Chamroukhi. Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data., June, 2014, Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML). pdf 
    @inproceedings{Chamroukhi-wkpICML-2014,
 author = {M. Bartcus and F. Chamroukhi},
 booktitle = {Proceedings of the uLearnBio workshop of the International Conference on Machine Learning (ICML)},
 pages = {},
 address = {Beijing},
 title = {Hierarchical Dirichlet Process Hidden Markov Model for unsupervised learning from bioacoustic data},
 year = {2014},
 month = {June},
 url = {https://chamroukhi.com/papers/ICMLwkshp_iHMM_2014.pdf},
}
  18. F. Chamroukhi, Marius Bartcus and Herve Glotin. Bayesian non-parametric parsimonious clustering., April, 2014, Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2014,
 author = {F. Chamroukhi and Marius Bartcus and Herve  Glotin},
 booktitle = {Proceedings of 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {},
 address = {Bruges, Belgium},
 title = {Bayesian non-parametric parsimonious clustering},
 year = {2014},
 month = {April},
 url = {https://chamroukhi.com/papers/IPGMM-esann2014},
 slides = {https://chamroukhi.com/conf-presentations/Friday_12h05_Chamroukhi_esann2014},
}
  19. M. Bartcus, F. Chamroukhi and H. Glotin. Clustering Bayésien Parcimonieux Non-Paramétrique., pages:3–13, Jan, 2014, Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering. pdf 
    @inproceedings{Chamroukhi-EGC-2014,
 author = {M. Bartcus and F. Chamroukhi and H. Glotin},
 title = {Clustering Bay{\'e}sien Parcimonieux Non-Param{\'e}trique},
 booktitle = {Extraction et Gestion des Connaissances (EGC), Atelier CluCo : Clustering et Co-clustering},
 pages = {3--13},
 address = {Rennes, France},
 month = {Jan},
 year = {2014},
 url = {https://chamroukhi.com/papers/IPGMM-EGC-CluCo-2014.pdf},
}
  20. F. Chamroukhi. Robust EM algorithm for model-based curve clustering., pages:1–8, August, 2013, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2013,
 author = {F. Chamroukhi},
 title = {Robust {EM} algorithm for model-based curve clustering},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Dallas, Texas},
 month = {August},
 year = {2013},
 url = {https://chamroukhi.com/papers/Chamroukhi-Robust-EM-MixReg-IJCNN2013.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
}
      
  21. Marius Bartcus, Faicel Chamroukhi and Hervé Glotin. Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture., pages:205–211, 2013, Proceedings of the NIPS4B workshop, Neural Information Processing Systems (NIPS). pdf 
    @inproceedings{Chamroukhi-wkpNIPS-2013,
 author = {Marius Bartcus and Faicel Chamroukhi and Herv{\'e}  Glotin},
 title = {Unsupervised whale song decomposition with Bayesian non-parametric Gaussian mixture},
 booktitle = {Proceedings of the NIPS4B workshop,  Neural Information Processing Systems (NIPS)},
 pages = {205--211},
 address = {Nevada, USA},
 year = {2013},
 url = {https://chamroukhi.com/papers/nips4b_wkshop.pdf},
}
  22. F. Chamroukhi and H. Glotin. Mixture model-based functional discriminant analysis for curve classification., pages:1–8, June, 2012, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2012,
 author = {F. Chamroukhi and H. Glotin},
 title = {Mixture model-based functional discriminant analysis for curve classification},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {1--8},
 address = {Brisbane, Australia},
 month = {June},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2012.pdf},
 slides = {https://chamroukhi.com/conf-presentations/talk-ijcnn-2013.pdf},
 software = {https://github.com/fchamroukhi/MixRHLP_Matlab},
}
  23. F. Chamroukhi, H. Glotin and C. Rabouy. Functional Mixture Discriminant Analysis with hidden process regression for curve classification., pages:281–286, April, 2012, Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012,
 author = {F. Chamroukhi and H.  Glotin and C. Rabouy},
 booktitle = {Proceedings of XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {281--286},
 address = {Bruges, Belgium},
 title = {Functional Mixture Discriminant Analysis with hidden process regression for curve classification},
 year = {2012},
 month = {April},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012.pdf},
 software = {https://github.com/fchamroukhi/FRHLP_Matlab},
}
  24. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Supervised and unsupervised classification approaches for human activity recognition using body-mounted sensors., pages:417–422, April, 2012, Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf 
    @inproceedings{Chamroukhi-ESANN-2012-2,
 author = {D. Trabelsi  and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 booktitle = {Proceedings of the XXth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {417--422},
 address = {Bruges, Belgium},
 title = {Supervised and unsupervised classification approaches for human activity recognition using
body-mounted sensors},
 month = {April},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-esann-2012-2.pdf},
}
  25. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. Classification automatique de données temporelles en classes ordonnées., Mai, 2012, Actes des 44 ème Journées de Statistique. pdf 
    @inproceedings{Chamroukhi-JDS-2012,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {Classification automatique de donn{\'e}es temporelles en classes ordonn{\'e}es},
 booktitle = {Actes des 44 {\`e}me Journ{\'e}es de Statistique},
 pages = { },
 address = {Bruxelles, Belgique},
 month = {Mai},
 year = {2012},
 url = {https://chamroukhi.com/papers/Chamroukhi-jds-2012.pdf},
}
  26. D. Trabelsi, S. Mohammed, F. Chamroukhi, L. Oukhellou and Y. Amirat. Activity Recognition Using Hidden Markov Models., september, 2011, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 
    @inproceedings{Trabelsi-wkpIROS-2011,
 author = {D. Trabelsi and S. Mohammed and F. Chamroukhi and L. Oukhellou and Y. Amirat},
 title = {Activity Recognition Using Hidden Markov Models},
 booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
 adress = {San Francisco, California},
 optpages = {},
 year = {2011},
 month = {september},
}
        
  27. F. Chamroukhi, A. Samé, G. Govaert and P. Aknin. A dynamic probabilistic modeling of railway switches operating states., May, 2011, Proccedings of the 9th World Congress on Railway Research (WCRR). pdf 
    @inproceedings{Chamroukhi-WCRR-2011,
 author = {F. Chamroukhi and A. Sam\'e and G. Govaert and P. Aknin},
 title = {A dynamic probabilistic modeling of railway switches operating states},
 booktitle = {Proccedings of the 9th World Congress on Railway Research (WCRR)},
 address = {Lille},
 year = {2011},
 month = {May},
 url = {https://chamroukhi.com/papers/WCRR-2011-Chamroukhi.pdf},
}
  28. F. Chamroukhi, A. Samé, P. Aknin and G. Govaert. Model-based clustering with Hidden Markov Model regression for time series with regime changes., pages:2814–2821, Jul-Aug, 2011, Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE. pdf 
    @inproceedings{Chamroukhi-IJCNN-2011,
 author = {F. Chamroukhi and A. Sam\'e  and P. Aknin and G. Govaert},
 title = {Model-based clustering with Hidden Markov Model regression for time series with regime changes},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
 pages = {2814--2821},
 adress = {San Jose, California, USA},
 year = {2011},
 month = {Jul-Aug},
 url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf},
}
        
  29. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for feature extraction from time series., pages:489–496, June, 2009, International Joint Conference on Neural Networks (IJCNN). pdf slides 
    @inproceedings{Chamroukhi-IJCNN-2009,
 author = {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
 title = {A regression model with a hidden logistic process for feature extraction from time series},
 booktitle = {International Joint Conference on Neural Networks (IJCNN)},
 year = {2009},
 month = {June},
 pages = {489--496},
 address = {Atlanta, GA},
 url = {https://chamroukhi.com/papers/chamroukhi_ijcnn2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_IJCNN2009},
}
          
  30. Samé, A., Chamroukhi, F. and Govaert, G.. Algorithme EM et modèle à processus latent pour la régression non linéaire., 2009, Actes des 41èmes Journées de Statistique de la SFDS. slides 
    @inproceedings{Chamroukhi-JDS-2009,
 author = {Sam\'e,  A. and Chamroukhi, F. and Govaert, G.},
 title = {Algorithme EM et mod\`ele \`a  processus latent pour la r\'egression non lin\'eaire},
 booktitle = {Actes des 41\`emes Journ\'ees de Statistique de la SFDS},
 pages = { },
 address = {Bordeaux},
 month = {},
 year = {2009},
 slides = {https://chamroukhi.com/conf-presentations/chamroukhi-slides-SFDS-2009.pdf},
}
          
  31. Chamroukhi, F., Samé, A. and Aknin, P.. A probabilistic approach for the classification of railway switch operating states. IEEE., 2009, The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. 
    @inproceedings{Chamroukhi-RCM-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Aknin, P.},
 booktitle = {The VIth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies},
 title = {A probabilistic approach for the classification of railway switch operating states},
 year = {2009},
 address = {Dublin, UK},
 publisher = {IEEE},
}
  32. Onanena, R., Chamroukhi, F., Oukhellou, L., Candusso, D., Aknin, P. and Hissel, D.. Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context. IEEE Computer Society., pages:632–637, 2009, Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA). pdf slides 
    @inproceedings{Chamroukhi-ICMLA-2009,
 author = {Onanena, R. and Chamroukhi, F. and Oukhellou, L. and Candusso, D. and Aknin, P. and Hissel, D.},
 title = {Estimation of Fuel Cell Life Time Using Latent Variables in Regression Context},
 booktitle = {Proceeding of the Eighth IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)},
 year = {2009},
 pages = {632--637},
 publisher = {IEEE Computer Society},
 address = {Miami, Flordia, USA},
 url = {https://chamroukhi.com/papers/IEEE-ICMLA2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ICMLA2009.pdf},
}
  33. Chamroukhi, F., Samé, A., Govaert, G. and Aknin, P.. A regression model with a hidden logistic process for signal parameterization., pages:503-508, 2009, Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). pdf slides 
    @inproceedings{Chamroukhi-ESANN-2009,
 author = {Chamroukhi, F. and Sam\'e, A. and Govaert, G. and Aknin, P.},
 booktitle = {Proceedings of XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 pages = {503-508},
 title = {A regression model with a hidden logistic process for signal parameterization},
 year = {2009},
 url = {https://chamroukhi.com/papers/chamroukhi_esann_2009.pdf},
 slides = {https://chamroukhi.com/conf-presentations/presentation_ESANN2009.pdf},
}
          
  34. Chamroukhi, F., Samé, A., Aknin, P. and Antoni, M.. Switch mechanism diagnosis using a pattern recognition approach. IEEE., pages:1–4, June, 2008, The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE). pdf slides 
    @inproceedings{Chamroukhi-RCM-2008,
 author = {Chamroukhi, F. and Sam\'e,  A. and Aknin, P. and Antoni, M.},
 title = {Switch mechanism diagnosis using a pattern recognition approach},
 booktitle = {The 4th IET International Conference on Railway Condition Monitoring RCM (IEEE)},
 year = {2008},
 address = {Derby, UK},
 month = {June},
 pages = {1--4},
 issn = {0537-9989},
 publisher = {IEEE},
 url = {https://chamroukhi.com/papers/chamroukhi_rcm_2008.pdf},
 slides = {https://chamroukhi.com/papers/presentation_rcm_2008.pdf},
}
  35. A. Samé, F. Chamroukhi and P. Aknin. Détection séquentielle de défauts sur des signaux de manøeuvres d'aiguillage., June, 2008, Workshop Surveillance, Sûreté et Sécurité des Grands Systèmes 3SGS'08. 
    @inproceedings{Chamroukhi-3SGS-2008,
 author = {A. Sam\'e and F. Chamroukhi and P. Aknin},
 title = {D\'etection s\'equentielle de d\'efauts sur des signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Workshop Surveillance, S\^uret\'e et S\'ecurit\'e des Grands Syst\`emes 3SGS'08},
 address = {Troyes, France},
 month = {June},
 year = {2008},
}
  36. F. Chamroukhi, A. Samé and P. Aknin. Régression à variable latente pour la modélisation de signaux de manøeuvres d'aiguillage., Vol. 112, pages:57–64, 2008, Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus sûrs, plus efficaces et plus attractifs. 
    @inproceedings{Chamroukhi-JD-INRETS-2008,
 author = {F. Chamroukhi and A. Sam\'e and P. Aknin},
 title = {R\'egression \`a variable latente pour la mod\'elisation de signaux de man{\oe}uvres d'aiguillage},
 booktitle = {Actes INRETS : Communiquer, naviguer, surveiller. Innovations pour des transports plus s\^urs, plus efficaces et plus attractifs},
 volume = {112},
 pages = {57--64},
 editor = {In J. Marais and M.~Berbineau, editors},
 year = {2008},
}
  1. Chamroukhi, F.. Statistical learning of latent data models for complex data analysis. Habilitation Thesis (HDR), Université de Toulon, 07 december, 2015 pdf 
    @phdthesis{Chamroukhi-HDR-2015,
 author = {Chamroukhi, F.},
 title = {Statistical learning of latent data models for complex data analysis},
 school = {Universit\'e de Toulon},
 month = {07 december},
 year = {2015},
 type = {{Habilitation Thesis (HDR)}},
 url = {https://chamroukhi.com/FChamroukhi-HDR.pdf},
}
  2. Chamroukhi, F.. Hidden process regression for curve modeling, classification and tracking. Ph.D. Thesis, Université de Technologie de Compiègne, 13 december, 2010 pdf 
    @phdthesis{Chamroukhi_PhD_2010,
 author = {Chamroukhi, F.},
 title = {Hidden process regression for curve modeling, classification and tracking},
 school = {Universit\'e de Technologie de Compi\`egne},
 month = {13 december},
 year = {2010},
 type = {Ph.D. Thesis},
 url = {https://chamroukhi.com/FChamroukhi-PhD.pdf},
}
  1. Faïcel Chamroukhi. On some research and innovation challenges in AI. Talk at Trinity College Dublin., October, 2024 pdf
  2. Faïcel Chamroukhi. On some statistical and machine learning problems in research and engineering: Challenges and industrial applications in Hybrid & Trustworthy AI. Talk at Koc University, KUIS AI Center., May, 2024 pdf
  3. Faïcel Chamroukhi. On some modern statistical and machine learning approaches for industrial applications. Talk at CISEM 2024 - Colloque International Statistique et Econométrie., May, 2024 pdf
  4. F. Chamroukhi. Learning mixtures-of-experts from heterogenous and high-dimensional data. CMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics., December, 2023 pdf
  5. F. Chamroukhi. On some approaches of deep learning for physical simulation in Industry. AI, Data, Robotics Forum: Generative AI for European society and industry. Workshop Leveraging AI-based Physical Simulation for Industry., November, 2023 pdf
  6. F. Chamroukhi. Leveraging statistical mixtures and learning techniques in some data science problems. Journeés de Statistique Mathématiques et Data Science., November, 2023 pdf
  7. P. Aknin and F. Chamroukhi. L'IA hybride. 8ème édition Data Science & Artificial Intelligence (DSAI) Day. P. Aknin and F. Chamroukhi., October, 2023 pdf
  8. F. Chamroukhi. Hybrid AI @ SystemX. Visit of the Czech AI Delegation to SystemX., October, 2023 pdf
  9. F. Chamroukhi. Chair of the Session "Generative Models 1" @ ECML PKDD 2023., September, 2023 pdf
  10. F. Chamroukhi. IRT SystemX: Votre transformation numérique par l'IA. La Matinale de la French Tech Paris Saclay: Les technologies IA au coeur de vos enjeux business., June, 2023 pdf
  11. F. Chamroukhi. Statistical Learning from heterogenous, high-dimensional and distributed data. Seminar Talk @ The DAVID laboratory/Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) - Université Paris Saclay., April, 2023 pdf
  12. F. Chamroukhi. On a new family of mixtures-of-experts models for learning from heterogeneous and distributed data. Heads'Up @ SystemX., Jan, 2023 pdf
  13. F. Chamroukhi. Sur une nouvelle famille de mélanges d'experts pour l'apprentissage à partir de données hétérogènes à l'échelle. Seminar @ Université Polytechnique Hauts-De-France / Lab of Maths., Dec, 2022 pdf
  14. F. Chamroukhi. The industrial challenges of deploying AI in critical systems. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  15. F. Chamroukhi. Scientific challenges of Confiance.AI. ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., September, 2022 pdf
  16. F. Chamroukhi. Apprentissage statistique en vue du passage à l'echelle: représentation, inférence et sélection. Seminar @ LIPN - The Computer Science Lan of the University of Sorbonne Paris Nord, Machine Learning Team., May, 2022 pdf
  17. F. Chamroukhi. Mixtures-of-experts with functional predictors. Talk at the 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)., December, 2021 pdf
  18. F. Chamroukhi. Optimized Learning of Structured Random Systems. Talk at Luxembourg Institute of Science and Technology (LIST)., March, 2021 pdf
  19. F. Chamroukhi. Statistical Analysis Tools for Complex Time-Series Data. Talk hosted by R-Ladies, Tunis Section., nov, 2020 pdf
  20. F. Chamroukhi. Towards a Random Ecosystem of Data Intelligence. Talk @ The Mathematical Sciences department Auckland University of Technology., 2020 pdf
  21. F. Chamroukhi. Mixtures-of-Experts for Functional Data. Talk @ The University of Queensland, School of Mathematics and Physics., August, 2019 pdf
  22. F. Chamroukhi. Unsupervised Statistical Learning from Heterogeneous Time-Series and Functional Data. Keynote note talk at the Research School on Statistics and Data Science-RSSDS 2019., July, 2019 pdf
  23. F. Chamroukhi. Functional Mixtures-of-Experts. Working group on Statistics & Data Sciences @ LMNO., June, 2019 pdf
  24. F. Chamroukhi. Functional Mixtures-of-Experts. Invited talk at the Model-based and multivariate functional data session, CRONOS Multivariate Data Analysis workshop., April, 2019 pdf
  25. F. Chamroukhi. Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Kickoff meeting of the project ANR SMILES., February, 2019 pdf
  26. F. Chamroukhi. Statistical data science and some unsupervised learning problems. Keynote note talk @ the International Symposium on Data Science and Computational Intelligence DSCI 2018., December, 2018 pdf
  27. F. Chamroukhi. ANR SMILES. Journée de l'ANR du 29 novembre 2018: Réunion de lancements de projets du CE 40., november, 2018 pdf
  28. F. Chamroukhi. Model-based (co-)clustering in some high-dimensional scenarios. Annual Summer Session of the Working Group on Model-Based Clustering, Ann Arbor, USA., July, 2018 pdf
  29. F. Chamroukhi. Introduction of S4D 2018. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  30. F. Chamroukhi. Unsupervised learning from high-dimensional and functional data. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  31. F. Chamroukhi. On some new Mixtures-of-Experts Models. International Research Summer School in Statistics & Data Science - S4D 2018., june, 2018 pdf
  32. F. Chamroukhi. Asterics: Apprentissage Statistique à l'Echelle pour la Représentation et la Classification non-supervisées. Normandie Université., 02 Oct, 2018 pdf
  33. F. Chamroukhi. SMILES: Statistical Modeling and Inference for unsupervised Learning at LargE-Scale. Talk at the days of the Normandy Federations of Mathematics (Normath) and Information Sciences and Technologies (Normastic)., 12 Oct, 2018 pdf
  34. F. Chamroukhi. Model-Based Co-Clustering of High-Dimensional Functional Data. ERCIM 2017 : The 10th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2017
  35. F. Chamroukhi. Introduction on Sata Science. International Research Summer School in Statistics & BigData Science - SBDS 2017., june, 2017 pdf
  36. F. Chamroukhi. Sciences des données. Journée Interlabs, Pôle de Compététivité Transactions Electroniques Securisées - TES., 27 Nov, 2017 pdf
  37. F. Chamroukhi. On some statistical data analysis and learning problems in Data Science. Plenary talk at the days of the Normandy Federation of Information Sciences and Technologies (Normastic), Rouen, France., 30 Mar, 2017 pdf
  38. F. Chamroukhi. On some mixtures for modeling complex datasets. Séminaire Statistique et Sciences des données, Laboratoire de Mathématiques Nicolas Oresme (UMR LMNO)., 02 feb, 2017 pdf
  39. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of Institut Elie Cartan, IECL - UMR CNRS 7502 - Nancy, France., April 18, 2016 pdf
  40. F. Chamroukhi. Statistical learning of generative models for signal processing. Seminar of the center of research in computer science, signal and automatic control of Lille, Signal, Models and Applications-SIGMA team., March 07, 2016 pdf
  41. F. Chamroukhi. Statistical learning of generative models for complex data analysis. Seminar of the lab of fundamental computer science of Marseille - LIF UMR CNRS 7279, Marseille, France., March 1st, 2016 pdf
  42. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Caen - LMNO UMR CNRS 6139, Caen, France., March 15, 2016 pdf
  43. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of the lab of mathematics of Evry - LaMME UMR CNRS 8071, Evry, France., March 17, 2016 pdf
  44. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Seminar of LORIA, UMR CNRS 7503 - Nancy, France., March 23, 2016 pdf
  45. F. Chamroukhi. Statistical learning of latent variable models for complex data analysis. Plenary talk at the days of the Federation of Mathematics of Normandie (FNM), Rouen, France., 06 Oct, 2016 pdf
  46. F. Chamroukhi. Hierarchical dynamical mixtures for functional data clustering and segmentation. Joint Statistics Seminar, Department of mathematics, KU Leuven, Leuven, Belgium., 01 Dec, 2016 pdf
  47. F. Chamroukhi. On some mixtures for modeling complex datasets. Seminar of Inria Lille-Nord Europe, Models ofr Data Analysis and Learning (Modal) team., 12 jan, 2016 pdf
  48. F. Chamroukhi. Robust non-normal mixtures of experts. ERCIM 2015 : The 8th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, London, UK., December, 2015 pdf slides
  49. F. Chamroukhi. Statistical learning of latent data models for complex data analysis. Seminar of the lab of Mathematics Paul Painlevé, UMR CNRS 8524., 04 Nov, 2015 pdf
  50. F. Chamroukhi. Model-based cluster and discriminant analysis for functional data. ERCIM 2014 : The 7th International Conference of the European Research Consortium for Informatics and Mathematics on Computational and Methodological Statistics, Pisa, Italy., December, 2014 pdf
  51. F. Chamroukhi. Mixture models for cluster analysis: from model-based inference to Bayesian non-parametrics. uLearnBio workshop of the International Conference on Machine Learning (ICML), Beijing., June, 2014 pdf
  52. F. Chamroukhi. Learning probabilistic latent process models from temporal data. VIIth International Summer School ERMITES 2012 on Representations and Decisions in Cognitive Vision, La seyne/Mer., september, 2012 pdf
  53. F. Chamroukhi. Apprentissage de modèles probabilistes à processus latent à partir de données temporelles. Seminaire GFD-LIPADE Université Paris 5., july, 2012 pdf
  54. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Feb, 2012 pdf
  55. F. Chamroukhi. Apprentissage de modèles génératifs à partir de données temporelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  56. F. Chamroukhi. Apprentissage de modèles probabilistes génératifs : Approches Bayésiennes Parcimonieuses pour des données fonctionnelles et des données multidimensionnelles. Séminaire du Laboratoire LSIS., Jan, 2012 pdf
  57. F. Chamroukhi. Diagnostic par analyse de données longitudinales. TISIC, Séminaire n 13, "Analyse de Données Longitudinales"., March, 2011 pdf
  58. F. Chamroukhi. Modèle probabiliste à base de processus latent pour la description et la classification de signaux. The Computer Science lab of Paris 13 University (LIPN)., April, 2010 pdf
  59. F. Chamroukhi. Modélisation probabiliste à base de processus latent de signaux monodimensionnels. Business Intelligence lab, Télécom-ParisTech., October, 2009 pdf
  60. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2009 pdf
  61. F. Chamroukhi. Modèle à processus latent pour la paramétrisation et l'apprentissage de signaux évolutifs. Application au diagnostic d'aiguillages. Séminaire DIAG, Inrets-LTN., April, 2009
  62. F. Chamroukhi. Modele de regression a processus logistique cache pour le diagnostic par suivi de point de fonctionnement des m�canismes d'aiguillages. Journée des doctorants LTN, INRETS., Nov, 2008 pdf
  63. F. Chamroukhi. Diagnostic par suivi de point de fonctionnement. Journée des doctorants Heudiasyc., July, 2008 pdf
  64. F. Chamroukhi. Régression à variable latente pour la modélisation des signaux de manøevre d'aiguillages. Journée des doctorants SPI, INRETS., July, 2008
  1. Faïcel Chamroukhi and Thien Nhat Pham. Distributed Learning of Mixtures of Experts. arXiv:2312.09877, December, 2023 pdf 
    @article{chamroukhi2023DMoE,
 title = {Distributed Learning of Mixtures of Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham},
 year = {2023},
 month = {December},
 eprint = { arXiv:2312.09877},
 journal = {},
 url = {https://arxiv.org/pdf/2312.09877.pdf},
}
      
  2. Faïcel Chamroukhi, Thien Nhat Pham, Van Ha Hoang and Geoffrey J McLachlan. Functional Mixtures-of-Experts. arXiv preprint arXiv:2202.02249 arXiv:2202.02249, February, 2022 pdf 
    @article{chamroukhi2022fME,
 title = {Functional Mixtures-of-Experts},
 author = {Fa\"icel Chamroukhi and Thien Nhat Pham and Van Ha Hoang and Geoffrey J McLachlan},
 year = {2022},
 month = {February},
 eprint = {arXiv:2202.02249},
 journal = {arXiv preprint arXiv:2202.02249},
 url = {https://arxiv.org/pdf/2202.02249.pdf},
}
  3. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien Duy and Forbes, Florence. Non-asymptotic model selection in block-diagonal mixture of polynomial experts models. arXiv preprint arXiv:2104.08959, may, 2021 pdf 
    @article{nguyen2021non-asympt-BD-MoE,
 title = {Non-asymptotic model selection in block-diagonal mixture of polynomial experts models},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien Duy and Forbes, Florence},
 eprint = {arXiv preprint arXiv:2104.08959},
 url = {https://arxiv.org/pdf/2104.08959.pdf},
 month = {may},
 year = {2021},
}
  4. Nguyen, TrungTin, Nguyen, Hien D, Chamroukhi, Faicel and McLachlan, Geoffrey J. An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models. arXiv preprint arXiv:2009.10622, 2020 pdf 
    @article{nguyen2020l_1MoE,
 title = {An $l\_1 $-oracle inequality for the Lasso in mixture-of-experts regression models},
 author = {Nguyen, TrungTin and Nguyen, Hien D and Chamroukhi, Faicel and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2009.10622},
 url = {https://arxiv.org/pdf/2009.10622.pdf},
 year = {2020},
}
  5. Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D and McLachlan, Geoffrey J. Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. arXiv preprint arXiv:2008.09787, 2020 pdf 
    @article{nguyen2020approximation,
 title = {Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces},
 author = {Nguyen, TrungTin and Chamroukhi, Faicel and Nguyen, Hien D and McLachlan, Geoffrey J},
 eprint = {arXiv preprint arXiv:2008.09787},
 url = {https://arxiv.org/pdf/2008.09787.pdf},
 year = {2020},
}
  6. T. Huynh and F. Chamroukhi. Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models. arXiv:1810.12161, July, 2019 pdf 
    @article{prEMME-2019,
 author = {T. Huynh and F. Chamroukhi},
 title = {Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models},
 volume = {},
 month = {July},
 year = {2019},
 url = {https://chamroukhi.com/papers/prEMME.pdf},
 eprint = { arXiv:1810.12161},
}
  7. Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan. Approximation by finite mixtures of continuous density functions that vanish at infinity. arXiv:1903.00147, March, 2019 pdf 
    @article{approx-by-fmmm-2019,
 author = {Trung Tin Nguyen, Hien D Nguyen, Faicel Chamroukhi, Geoffrey J McLachlan},
 title = {Approximation by finite mixtures of continuous density functions that vanish at infinity},
 volume = {},
 month = {March},
 year = {2019},
 url = {https://arxiv.org/pdf/1903.00147.pdf},
 eprint = {arXiv:1903.00147},
}
  8. F. Chamroukhi, M. Bartcus and H. Glotin. Dirichlet Process Parsimonious Gaussian Mixture for clustering. arXiv:1501.03347v2, 2018 pdf 
    @article{Chamroukhi-DPPM-2018,
 author = {F. Chamroukhi and M. Bartcus and H. Glotin},
 eprint = {arXiv:1501.03347v2},
 publisher = {},
 title = {Dirichlet Process Parsimonious Gaussian Mixture for clustering},
 volume = {},
 url = {https://chamroukhi.com/papers/DPPM.pdf},
 year = {2018},
 note = {submitted},
}
  9. F. Chamroukhi. Bayesian mixtures of spatial spline regressions. arXiv:1508.00635, Aug, 2015 pdf 
    @article{Chamroukhi-BSSRM-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1508.00635},
 title = {Bayesian mixtures of spatial spline regressions},
 volume = {},
 url = {http://arxiv.org/pdf/1508.00635.pdf},
 month = {Aug},
 year = {2015},
}
  10. F. Chamroukhi. Non-Normal Mixtures of Experts. arXiv:1506.06707, July, 2015 pdf 
    @article{Chamroukhi-NNMoE-2015,
 author = {F. Chamroukhi},
 eprint = {arXiv:1506.06707},
 title = {Non-Normal Mixtures of Experts},
 volume = {},
 url = {http://arxiv.org/pdf/1506.06707.pdf},
 month = {July},
 year = {2015},
 note = {Report (61 pages)},
}