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Publications of Irene Balelli

Articles in journal, book chapters

  1. Quentin Clairon, Chloé Pasin, Irene Balelli, Rodolphe Thiébaut, and Mélanie Prague. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach. Computational Statistics, September 2023. Keyword(s): Dynamic population models, Ordinary differential equations, Optimal control theory, Mechanistic models, Nonlinear mixed effects models, Clinical trial analysis. [bibtex-entry]


  2. Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L Aguila, and Andre Altmann. Integration of Multimodal Data. In Olivier Colliot, editor, Machine Learning for Brain Disorders, volume NM197 of Neuromethods, pages 573 - 597. Springer, 2023. Keyword(s): Multivariate analysis, Latent variable models, Multimodal imaging, -Omics, Imaginggenetics, Partial least squares, Canonical correlation analysis, Variational autoencoders, Sparsity, Interpretability. [bibtex-entry]


  3. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. Journal of Machine Learning for Biomedical Imaging, April 2022. [bibtex-entry]


Conference articles

  1. Safaa Al-Ali, Jordi Llopis-Lorente, Maria Teresa Mora, Maxime Sermesant, Beatriz Trénor, and Irene Balelli. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. In 2023 Computing in Cardiology (CinC), 2023 Computing in Cardiology (CinC), Atlanta (GA), United States, October 2023. Keyword(s): Causal discovery, Drug safety, Ions channel, TdP risk. [bibtex-entry]


  2. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. In International Conference on Information Processing in Medical Imaging, Information processing in medical imaging: proceedings of the 27th International Conference, IPMI 2021, Bornholm, Denmark, June 2021. Keyword(s): Federated Learning, Hierarchical Generative Model, Heterogeneity. [bibtex-entry]


Miscellaneous

  1. Irene Balelli, Aude Sportisse, Francesco Cremonesi, Pierre-Alexandre Mattei, and Marco Lorenzi. Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. Note: Working paper or preprint, 2023. Keyword(s): Missing data, Federated learning, Federated pre-processing, Variational autoencoders, Deep Learning. [bibtex-entry]


  2. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. Note: Working paper or preprint, April 2023. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. [bibtex-entry]



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