11 citations · 13 across the 4 of their papers we have counts for
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cs.LG2023★ 11 cited
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
Francesco Cremonesi, Marc Vesin, Sergen Cansiz +16
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, t…
stat.ML2023★ 1 cited
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Aude Sportisse, Hugo Schmutz, Olivier Humbert +2
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels,…