15 citations · 22 across the 8 of their papers we have counts for
4 papers · 1 filter
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
Irene Balelli, Aude Sportisse, Francesco Cremonesi +2
Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processin…
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,…
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
Imputation and low-rank estimation with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank…