Search Results for author: Aude Sportisse

Found 7 papers, 4 papers with code

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models

no code implementations17 Apr 2023 Irene Balelli, Aude Sportisse, Francesco Cremonesi, Pierre-Alexandre Mattei, Marco Lorenzi

In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.

Federated Learning Imputation

Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism

no code implementations15 Feb 2023 Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei

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, which occur when some classes are more likely to be labeled than others.

Data Augmentation

Model-based Clustering with Missing Not At Random Data

1 code implementation20 Dec 2021 Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki

In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.

Clustering Imputation

Debiasing Averaged Stochastic Gradient Descent to handle missing values

no code implementations NeurIPS 2020 Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josses

Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.

Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data

1 code implementation NeurIPS 2020 Aude Sportisse, Claire Boyer, Julie Josse

Considering a data matrix generated from a probabilistic principal component analysis (PPCA) model containing several MNAR variables, not necessarily under the same self-masked missing mechanism, we propose estimators for the means, variances and covariances of the variables and study their consistency.

Statistics Theory Statistics Theory

Imputation and low-rank estimation with Missing Not At Random data

1 code implementation29 Dec 2018 Aude Sportisse, Claire Boyer, Julie Josse

Our second contribution is to suggest a computationally efficient surrogate estimation by implicitly taking into account the joint distribution of the data and the missing mechanism: the data matrix is concatenated with the mask coding for the missing values; a low-rank structure for exponential family is assumed on this new matrix, in order to encode links between variables and missing mechanisms.

Imputation Matrix Completion

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