15 citations · 22 across the 8 of their papers we have counts for
11 papers
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
Semi-supervised learning has emerged as a powerful paradigm for leveraging large amounts of unlabeled data to improve the performance of machine learning models when labeled data a…
Amortized Variational Inference for Logistic Regression with Missing Covariates
M. Cherifi, Aude Sportisse, Xujia Zhu +2
Missing covariate data pose a significant challenge to statistical inference and machine learning, particularly for classification tasks like logistic regression. Classical iterati…
Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
Alexandre Hippert-Ferrer, Aude Sportisse, Amirhossein Javaheri +2
This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missin…
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…
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
Pascaline Descloux, Claire Boyer, Julie Josse +2
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology, initially introduced for sparse linear models, to the sparse corruptions problem. We give theoretical guar…
Debiasing Stochastic Gradient Descent to handle missing values
Julie Josse, Aude Sportisse, Claire Boyer +1
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.However, a major caveat of large data is their…
R-miss-tastic: a unified platform for missing values methods and workflows
Imke Mayer, Aude Sportisse, Julie Josse +2
Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and vi…
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing Not At Random (MNAR) values lead to significant biases in the data, since the probability of missingness depends on the unobserved values.They are ''not ignorable'' in the…
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…