b'Julie Josse'

DeepAI AI Chat

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research

∙ 06/05/2023

Conformal Prediction with Missing Values

Conformal prediction is a theoretically grounded framework for construct...

0 Margaux Zaffran, et al. ∙

research

∙ 03/28/2023

Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?

There are many measures to report so-called treatment or causal effect: ...

0 Bénédicte Colnet, et al. ∙

research

∙ 01/13/2023

Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data

An individualized treatment regime (ITR) is a decision rule that assigns...

0 Pan Zhao, et al. ∙

research

∙ 08/16/2022

Reweighting the RCT for generalization: finite sample analysis and variable selection

The limited scope of Randomized Controlled Trials (RCT) is increasingly ...

0 Bénédicte Colnet, et al. ∙

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...

0 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...

0 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...

0 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 ...

0 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...

0 Bénédicte Colnet, et al. ∙

research

∙ 04/26/2021

Transporting treatment effects with incomplete attributes

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

0 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...

0 Bénédicte Colnet, et al. ∙

research

∙ 11/12/2020

VARCLUST: clustering variables using dimensionality reduction

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

0 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...

11 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...

0 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...

0 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 ...

0 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...

16 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...

17 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...

0 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...

0 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 ...

0 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...

0 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...

0 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...

0 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-...

0 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...

0 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...

0 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...

0 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...

0 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...

0 Julie Josse, et al. ∙