Search Results for author:
Found 7 papers, 4 papers with code
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
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.
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
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.
Model-based Clustering with Missing Not At Random Data
In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates.
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
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
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.
