Publications of Irene Balelli

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. 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]

1. 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]

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. Note: Working paper or preprint, 2023. Keyword(s): Causal discovery, Drug safety, Ions channel, TdP risk. [bibtex-entry]



2. 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]



3. 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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