4 citations · 7 across the 10 of their papers we have counts for
11 papers
A Statistical Learning Take on the Concordance Index for Survival Analysis
Alex Nowak-Vila, Kevin Elgui, Genevieve Robin
The introduction of machine learning (ML) techniques to the field of survival analysis has increased the flexibility of modeling approaches, and ML based models have become state-o…
Generative methods for sampling transition paths in molecular dynamics
Tony Lelièvre, Geneviève Robin, Inass Sekkat +2
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastabili…
Inference of Multiscale Gaussian Graphical Model
Do Edmond Sanou, Christophe Ambroise, Geneviève Robin
Gaussian Graphical Models (GGMs) are widely used in high-dimensional data analysis to synthesize the interaction between variables. In many applications, such as genomics or image…
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines +1
The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent vari…
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…
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…
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…
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…
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…
Incomplete graphical model inference via latent tree aggregation
Geneviève Robin, Christophe Ambroise, Stéphane Robin
Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expressi…
Low-rank model with covariates for count data analysis
Geneviève Robin, Julie Josse, Eric Moulines +1
Count data are collected in many scientific and engineering tasks including image processing, single-cell RNA sequencing and ecological studies. Such data sets often contain missin…