b'Julie Josse'

research

∙ 02/17/2022

Benchmarking missing-values approaches for predictive models on health databases

BACKGROUND: As databases grow larger, it becomes harder to fully control...

Alexandre Perez-Lebel, et al. ∙

research

∙ 02/15/2022

Adaptive Conformal Predictions for Time Series

Uncertainty quantification of predictive models is crucial in decision-m...

Margaux Zaffran, et al. ∙

research

∙ 12/20/2021

Model-based Clustering with Missing Not At Random Data

In recent decades, technological advances have made it possible to colle...

Aude Sportisse, et al. ∙

research

∙ 06/01/2021

What's a good imputation to predict with missing values?

How to learn a good predictor on data with missing values? Most efforts ...

Marine Le Morvan, et al. ∙

research

∙ 05/13/2021

Generalizing a causal effect: sensitivity analysis and missing covariates

While a randomized controlled trial (RCT) readily measures the average t...

Bénédicte Colnet, et al. ∙

research

∙ 04/26/2021

Transporting treatment effects with incomplete attributes

The simultaneous availability of experimental and observational data to ...

Imke Mayer, et al. ∙

research

∙ 11/16/2020

Causal inference methods for combining randomized trials and observational studies: a review

With increasing data availability, treatment causal effects can be evalu...

Bénédicte Colnet, et al. ∙

research

∙ 11/12/2020

VARCLUST: clustering variables using dimensionality reduction

VARCLUST algorithm is proposed for clustering variables under the assump...

Piotr Sobczyk, et al. ∙

research

∙ 07/03/2020

Neumann networks: differential programming for supervised learning with missing values

The presence of missing values makes supervised learning much more chall...

Marine Le Morvan, et al. ∙

research

∙ 05/12/2020

Robust Lasso-Zero for sparse corruption and model selection with missing covariates

We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...

Pascaline Descloux, et al. ∙

research

∙ 02/25/2020

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models

Inferring causal effects of a treatment, intervention or policy from obs...

Imke Mayer, et al. ∙

research

∙ 02/21/2020

Debiasing Stochastic Gradient Descent to handle missing values

A major caveat of large scale data is their incom-pleteness. We propose ...

Aude Sportisse, et al. ∙

research

∙ 02/10/2020

Missing Data Imputation using Optimal Transport

Missing data is a crucial issue when applying machine learning algorithm...

Boris Muzellec, et al. ∙

research

∙ 02/03/2020

Linear predictor on linearly-generated data with missing values: non consistency and solutions

We consider building predictors when the data have missing values. We st...

Marine Le Morvan, et al. ∙

research

∙ 10/23/2019

Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in mo...

Imke Mayer, et al. ∙

research

∙ 09/14/2019

Adaptive Bayesian SLOPE – High-dimensional Model Selection with Missing Values

The selection of variables with high-dimensional and missing data is a m...

Wei Jiang, et al. ∙

research

∙ 08/13/2019

R-miss-tastic: a unified platform for missing values methods and workflows

Missing values are unavoidable when working with data. Their occurrence ...

Imke Mayer, et al. ∙

research

∙ 06/06/2019

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

Missing Not At Random values are considered to be non-ignorable and requ...

Aude Sportisse, et al. ∙

research

∙ 06/06/2019

Estimation with informative missing data in the low-rank model with random effects

Matrix completion based on low-rank models is very popular and comes wit...

Aude Sportisse, et al. ∙

research

∙ 02/19/2019

On the consistency of supervised learning with missing values

In many application settings, the data are plagued with missing features...

Julie Josse, et al. ∙

research

∙ 12/29/2018

Imputation and low-rank estimation with Missing Non At Random data

Missing values challenge data analysis because many supervised and unsu-...

Aude Sportisse, et al. ∙

research

∙ 12/20/2018

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames

Many applications of machine learning involve the analysis of large data...

Geneviève Robin, et al. ∙

research

∙ 06/26/2018

Main effects and interactions in mixed and incomplete data frames

A mixed data frame (MDF) is a table collecting categorical, numerical an...

Geneviève Robin, et al. ∙

research

∙ 05/11/2018

Stochastic Approximation EM for Logistic Regression with Missing Values

Logistic regression is a common classification method in supervised lear...

Wei Jiang, et al. ∙

research

∙ 04/30/2018

Imputation of mixed data with multilevel singular value decomposition

Statistical analysis of large data sets offers new opportunities to bett...

François Husson, et al. ∙

research

∙ 10/30/2014

Bootstrap-Based Regularization for Low-Rank Matrix Estimation

We develop a flexible framework for low-rank matrix estimation that allo...

Julie Josse, et al. ∙

Julie Josse

Featured Co-authors

Wei Jiang

Marco Cuturi

Gaël Varoquaux

Shu Yang

Stefan Wager

Eric Moulines

Robert Tibshirani

Jean-Philippe Vert

Hoi-To Wai

Erwan Scornet

Thomas Moreau

Benchmarking missing-values approaches for predictive models on health databases

Adaptive Conformal Predictions for Time Series

Model-based Clustering with Missing Not At Random Data

What's a good imputation to predict with missing values?

Generalizing a causal effect: sensitivity analysis and missing covariates

Transporting treatment effects with incomplete attributes

Causal inference methods for combining randomized trials and observational studies: a review

VARCLUST: clustering variables using dimensionality reduction

Neumann networks: differential programming for supervised learning with missing values

Robust Lasso-Zero for sparse corruption and model selection with missing covariates

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models

Debiasing Stochastic Gradient Descent to handle missing values

Missing Data Imputation using Optimal Transport

Linear predictor on linearly-generated data with missing values: non consistency and solutions

Doubly robust treatment effect estimation with missing attributes

Adaptive Bayesian SLOPE – High-dimensional Model Selection with Missing Values

R-miss-tastic: a unified platform for missing values methods and workflows

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

Estimation with informative missing data in the low-rank model with random effects

On the consistency of supervised learning with missing values

Imputation and low-rank estimation with Missing Non At Random data

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames

Main effects and interactions in mixed and incomplete data frames

Stochastic Approximation EM for Logistic Regression with Missing Values

Imputation of mixed data with multilevel singular value decomposition

Bootstrap-Based Regularization for Low-Rank Matrix Estimation