
Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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VARCLUST: clustering variables using dimensionality reduction
VARCLUST algorithm is proposed for clustering variables under the assump...
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Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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Robust LassoZero for sparse corruption and model selection with missing covariates
We propose Robust LassoZero, an extension of the LassoZero methodology...
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MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from obs...
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Debiasing Stochastic Gradient Descent to handle missing values
A major caveat of large scale data is their incompleteness. We propose ...
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Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithm...
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Linear predictor on linearlygenerated data with missing values: non consistency and solutions
We consider building predictors when the data have missing values. We st...
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Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
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Adaptive Bayesian SLOPE – Highdimensional Model Selection with Missing Values
The selection of variables with highdimensional and missing data is a m...
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Rmisstastic: a unified platform for missing values methods and workflows
Missing values are unavoidable when working with data. Their occurrence ...
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Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Missing Not At Random values are considered to be nonignorable and requ...
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Estimation with informative missing data in the lowrank model with random effects
Matrix completion based on lowrank models is very popular and comes wit...
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On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
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Imputation and lowrank estimation with Missing Non At Random data
Missing values challenge data analysis because many supervised and unsu...
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Lowrank Interaction with Sparse Additive Effects Model for Large Data Frames
Many applications of machine learning involve the analysis of large data...
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Main effects and interactions in mixed and incomplete data frames
A mixed data frame (MDF) is a table collecting categorical, numerical an...
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Stochastic Approximation EM for Logistic Regression with Missing Values
Logistic regression is a common classification method in supervised lear...
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Imputation of mixed data with multilevel singular value decomposition
Statistical analysis of large data sets offers new opportunities to bett...
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BootstrapBased Regularization for LowRank Matrix Estimation
We develop a flexible framework for lowrank matrix estimation that allo...
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Julie Josse
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