activity
20182021
most citedLow-rank Interaction with Sparse Additive Effects Model for Large Data Frames

4 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

stat.AP2021

Technical report: Impact of evaluation metrics and sampling on the comparison of machine learning methods for biodiversity indicators prediction

Geneviève Robin, Cathia Le Hasif

Machine learning (ML) approaches are used more and more widely in biodiversity monitoring. In particular, an important application is the problem of predicting biodiversity indicat…

stat.ML2019

Outliers Detection in Networks with Missing Links

Solenne Gaucher, Olga Klopp, Geneviève Robin

Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analys…

stat.ML20184 cited

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames

Geneviève Robin, Hoi-To Wai, Julie Josse +2

Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with…

stat.ME2018

Main effects and interactions in mixed and incomplete data frames

Geneviève Robin, Olga Klopp, Julie Josse +2

A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abun…

stat.AP2018

Imputation of mixed data with multilevel singular value decomposition

François Husson, Julie Josse, Balasubramanian Narasimhan +1

Statistical analysis of large data sets offers new opportunities to better understand many processes. Yet, data accumulation often implies relaxing acquisition procedures or compou…