A Probabilistic Framework for Modeling the Variability Across Federated Datasets. - Irene Balelli; Santiago Silva 001; Marco Lorenzi (IPMI, 2021)

A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. - Irene Balelli; Santiago Silva 001; Marco Lorenzi (CoRR, 2022)

A Causal Discovery Approach to Streamline Ionic Currents Selection to Improve Drug-Induced TdP Risk Assessment. - Safaa Al-Ali; Jordi Llopis-Lorente; Maria Teresa Mora; Maxime Sermesant; Beatriz Trénor; Irene Balelli (CinC, 2023)

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. - Irene Balelli; Aude Sportisse; Francesco Cremonesi; Pierre-Alexandre Mattei; Marco Lorenzi (CoRR, 2023)

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. - Francesco Cremonesi; Marc Vesin; Sergen Cansiz; Yannick Bouillard; Irene Balelli; Lucia Innocenti; Santiago Silva 001; Samy-Safwan Ayed; Riccardo Taiello; Laetitia Kameni; Richard Vidal; Fanny Orlhac; et al. (CoRR, 2023)

Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach. - Quentin Clairon; Chloé Pasin; Irene Balelli; Rodolphe Thiébaut; Mélanie Prague (Comput. Stat., 2024)

Cardiac Electromechanical Model Sensitivity Analysis Using Causal Discovery. - Safaa Al-Ali; Jesus Jairo Rodríguez Padilla; Maxime Sermesant; Irene Balelli (FIMH (1), 2025)