b'Julie Josse'

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

How to learn a good predictor on data with missing values? Most efforts ...
06/01/2021 ∙ by Marine Le Morvan, et al. ∙ 0 ∙ share

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Generalizing a causal effect: sensitivity analysis and missing covariates

While a randomized controlled trial (RCT) readily measures the average t...
05/13/2021 ∙ by Bénédicte Colnet, et al. ∙ 0 ∙ share

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Transporting treatment effects with incomplete attributes

The simultaneous availability of experimental and observational data to ...
04/26/2021 ∙ by Imke Mayer, et al. ∙ 0 ∙ share

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

With increasing data availability, treatment causal effects can be evalu...
11/16/2020 ∙ by Bénédicte Colnet, et al. ∙ 0 ∙ share

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VARCLUST: clustering variables using dimensionality reduction

VARCLUST algorithm is proposed for clustering variables under the assump...
11/12/2020 ∙ by Piotr Sobczyk, et al. ∙ 0 ∙ share

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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...
07/03/2020 ∙ by Marine Le Morvan, et al. ∙ 11 ∙ share

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

We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...
05/12/2020 ∙ by Pascaline Descloux, et al. ∙ 0 ∙ share

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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...
02/25/2020 ∙ by Imke Mayer, et al. ∙ 0 ∙ share

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Debiasing Stochastic Gradient Descent to handle missing values

A major caveat of large scale data is their incom-pleteness. We propose ...
02/21/2020 ∙ by Aude Sportisse, et al. ∙ 0 ∙ share

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Missing Data Imputation using Optimal Transport

Missing data is a crucial issue when applying machine learning algorithm...
02/10/2020 ∙ by Boris Muzellec, et al. ∙ 16 ∙ share

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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...
02/03/2020 ∙ by Marine Le Morvan, et al. ∙ 17 ∙ share

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Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in mo...
10/23/2019 ∙ by Imke Mayer, et al. ∙ 0 ∙ share

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

The selection of variables with high-dimensional and missing data is a m...
09/14/2019 ∙ by Wei Jiang, et al. ∙ 0 ∙ share

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

Missing values are unavoidable when working with data. Their occurrence ...
08/13/2019 ∙ by Imke Mayer, et al. ∙ 0 ∙ share

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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 non-ignorable and requ...
06/06/2019 ∙ by Aude Sportisse, et al. ∙ 0 ∙ share

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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...
06/06/2019 ∙ by Aude Sportisse, et al. ∙ 0 ∙ share

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On the consistency of supervised learning with missing values

In many application settings, the data are plagued with missing features...
02/19/2019 ∙ by Julie Josse, et al. ∙ 0 ∙ share

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

Missing values challenge data analysis because many supervised and unsu-...
12/29/2018 ∙ by Aude Sportisse, et al. ∙ 0 ∙ share

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

Many applications of machine learning involve the analysis of large data...
12/20/2018 ∙ by Geneviève Robin, et al. ∙ 0 ∙ share

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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...
06/26/2018 ∙ by Geneviève Robin, et al. ∙ 0 ∙ share

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Stochastic Approximation EM for Logistic Regression with Missing Values

Logistic regression is a common classification method in supervised lear...
05/11/2018 ∙ by Wei Jiang, et al. ∙ 0 ∙ share

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Imputation of mixed data with multilevel singular value decomposition

Statistical analysis of large data sets offers new opportunities to bett...
04/30/2018 ∙ by François Husson, et al. ∙ 0 ∙ share

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Bootstrap-Based Regularization for Low-Rank Matrix Estimation

We develop a flexible framework for low-rank matrix estimation that allo...
10/30/2014 ∙ by Julie Josse, et al. ∙ 0 ∙ share

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Julie Josse

Featured Co-authors

Marco Cuturi

Wei Jiang

Gaël Varoquaux

Stefan Wager

Eric Moulines

Shu Yang

Jean-Philippe Vert

Robert Tibshirani

Hoi-To Wai

Erwan Scornet

Veronika Rockova

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