22 citations
- Precision Spine (United States)US3 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Université de MontpellierFR2 papers
- Biologie LaborCH1 paper
- CEA Paris-SaclayFR1 paper
- CELESTE: statistique mathématique et apprentissageFR1 paper
- Centre de Mathématiques Appliquées de l'École polytechniqueFR1 paper
- Centre de Recherche en Économie et StatistiqueFR1 paper
- Centre for Research and Information on Substance AbuseNG1 paper
- Centre Hospitalier Universitaire de MontpellierFR1 paper
- Centre Inria d'Université Côte d'AzurFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
7 papers
Do we Need Dozens of Methods for Real World Missing Value Imputation?
Krystyna Grzesiak, Christophe Muller, Julie Josse +1
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
Freezing of Gait as a Complication of Pallidal Deep Brain Stimulation in DYT- KMT2B Patients with Evidence of Striatonigral Degeneration
Laura Cif, Diane Demailly, Xavier Vasques +4
Background: Mutations in KMT2B are a recognized cause of early-onset complex dystonia, with deep brain stimulation (DBS) of the internal globus pallidus (GPi-DBS) being an effectiv…
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) es…
Nebula: Efficient, Private and Accurate Histogram Estimation
Ali Shahin Shamsabadi, Peter Snyder, Ralph Giles +2
We present \textit{Nebula}, a system for differentially private histogram estimation on data distributed among clients. \textit{Nebula} allows clients to independently decide wheth…
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…
What's a good imputation to predict with missing values?
Marine Le Morvan, Julie Josse, Erwan Scornet +1
How to learn a good predictor on data with missing values? Most efforts focus on first imputing as well as possible and second learning on the completed data to predict the outcome…