Erwan Scornet | DeepAI

DeepAI AI Chat

Featured Co-authors

Gaël Varoquaux
55 publications
Jean-Philippe Vert
34 publications
Julie Josse
31 publications
Stéphane Gaïffas
29 publications
Thomas Moreau
22 publications
Aymeric Dieuleveut
19 publications
Claire Boyer
19 publications
Gérard Biau
19 publications
Johannes Welbl
18 publications
Jaouad Mourtada
13 publications
Sébastien Da Veiga
11 publications

∙ 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. ∙

∙ 01/31/2023

Naive imputation implicitly regularizes high-dimensional linear models

Two different approaches exist to handle missing values for prediction: ...

0 Alexis Ayme, et al. ∙

∙ 09/30/2022

Sparse tree-based initialization for neural networks

Dedicated neural network (NN) architectures have been designed to handle...

0 Patrick Lutz, et al. ∙

∙ 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. ∙

∙ 02/08/2022

Is interpolation benign for random forests?

Statistical wisdom suggests that very complex models, interpolating trai...

0 Ludovic Arnould, et al. ∙

∙ 02/03/2022

Minimax rate of consistency for linear models with missing values

Missing values arise in most real-world data sets due to the aggregation...

0 Alexis Ayme, et al. ∙

∙ 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. ∙

∙ 05/25/2021

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests

Interpretability of learning algorithms is crucial for applications invo...

0 Clément Bénard, et al. ∙

∙ 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. ∙

∙ 02/26/2021

MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA

Variable importance measures are the main tools to analyze the black-box...

0 Clément Bénard, et al. ∙

∙ 10/29/2020

Analyzing the tree-layer structure of Deep Forests

Random forests on the one hand, and neural networks on the other hand, h...

12 Ludovic Arnould, et al. ∙

∙ 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. ∙

∙ 04/29/2020

Interpretable Random Forests via Rule Extraction

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a...

0 Clément Bénard, et al. ∙

∙ 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. ∙

∙ 01/13/2020

Trees, forests, and impurity-based variable importance

Tree ensemble methods such as random forests [Breiman, 2001] are very po...

17 Erwan Scornet, et al. ∙

∙ 08/19/2019

SIRUS: making random forests interpretable

State-of-the-art learning algorithms, such as random forests or neural n...

0 Clément Bénard, et al. ∙

∙ 06/25/2019

AMF: Aggregated Mondrian Forests for Online Learning

Random Forests (RF) is one of the algorithms of choice in many supervise...

0 Jaouad Mourtada, et al. ∙

∙ 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. ∙

∙ 03/15/2018

Minimax optimal rates for Mondrian trees and forests

Introduced by Breiman (2001), Random Forests are widely used as classifi...

0 Jaouad Mourtada, et al. ∙

∙ 11/08/2017

Universal consistency and minimax rates for online Mondrian Forests

We establish the consistency of an algorithm of Mondrian Forests, a rand...

0 Jaouad Mourtada, et al. ∙

∙ 04/25/2016

Neural Random Forests

Given an ensemble of randomized regression trees, it is possible to rest...

0 Gérard Biau, et al. ∙

∙ 11/18/2015

A Random Forest Guided Tour

The random forest algorithm, proposed by L. Breiman in 2001, has been ex...

0 Gérard Biau, et al. ∙

∙ 05/12/2014

Consistency of random forests

Random forests are a learning algorithm proposed by Breiman [Mach. Learn...

0 Erwan Scornet, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world