Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. - Andrew Jesson; Alyson Douglas; Peter Manshausen; Maëlys Solal; Nicolai Meinshausen; Philip Stier; Yarin Gal; Uri Shalit (NeurIPS, 2022)

Using uncertainty-aware machine learning models to study aerosol-cloud interactions. - Maëlys Solal; Andrew Jesson; Yarin Gal; Alyson Douglas (CoRR, 2023)

Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET. - Maëlys Solal; Ravi Hassanaly; Ninon Burgos (CoRR, 2023)

Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI? - Evangelia Christodoulou; Annika Reinke; Rola Houhou; Piotr Kalinowski; Selen Erkan; Carole H. Sudre; Ninon Burgos; Sofiène Boutaj; Sophie Loizillon; Maëlys Solal; Nicola Rieke; Veronika Cheplygina; et al. (MICCAI (10), 2024)

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET. - Ravi Hassanaly; Camille Brianceau; Maëlys Solal; Olivier Colliot; Ninon Burgos (CoRR, 2024)

False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims. - Evangelia Christodoulou; Annika Reinke; Pascaline Andrè; Patrick Godau; Piotr Kalinowski; Rola Houhou; Selen Erkan; Carole H. Sudre; Ninon Burgos; Sofiène Boutaj; Sophie Loizillon; Maëlys Solal; et al. (CoRR, 2025)