
What's a good imputation to predict with missing values?
How to learn a good predictor on data with missing values? Most efforts ...
read it

Generalizing a causal effect: sensitivity analysis and missing covariates
While a randomized controlled trial (RCT) readily measures the average t...
read it

Transporting treatment effects with incomplete attributes
The simultaneous availability of experimental and observational data to ...
read it

Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
read it

VARCLUST: clustering variables using dimensionality reduction
VARCLUST algorithm is proposed for clustering variables under the assump...
read it

Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
read it

Robust LassoZero for sparse corruption and model selection with missing covariates
We propose Robust LassoZero, an extension of the LassoZero methodology...
read it

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from obs...
read it

Debiasing Stochastic Gradient Descent to handle missing values
A major caveat of large scale data is their incompleteness. We propose ...
read it

Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithm...
read it

Linear predictor on linearlygenerated data with missing values: non consistency and solutions
We consider building predictors when the data have missing values. We st...
read it

Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
read it

Adaptive Bayesian SLOPE – Highdimensional Model Selection with Missing Values
The selection of variables with highdimensional and missing data is a m...
read it

Rmisstastic: a unified platform for missing values methods and workflows
Missing values are unavoidable when working with data. Their occurrence ...
read it

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...
read it

Estimation with informative missing data in the lowrank model with random effects
Matrix completion based on lowrank models is very popular and comes wit...
read it

On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
read it

Imputation and lowrank estimation with Missing Non At Random data
Missing values challenge data analysis because many supervised and unsu...
read it

Lowrank Interaction with Sparse Additive Effects Model for Large Data Frames
Many applications of machine learning involve the analysis of large data...
read it

Main effects and interactions in mixed and incomplete data frames
A mixed data frame (MDF) is a table collecting categorical, numerical an...
read it

Stochastic Approximation EM for Logistic Regression with Missing Values
Logistic regression is a common classification method in supervised lear...
read it

Imputation of mixed data with multilevel singular value decomposition
Statistical analysis of large data sets offers new opportunities to bett...
read it

BootstrapBased Regularization for LowRank Matrix Estimation
We develop a flexible framework for lowrank matrix estimation that allo...
read it
Julie Josse
is this you? claim profile