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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: ...
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01/31/2023
Naive imputation implicitly regularizes high-dimensional linear models
Two different approaches exist to handle missing values for prediction: ...
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09/30/2022
Sparse tree-based initialization for neural networks
Dedicated neural network (NN) architectures have been designed to handle...
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08/16/2022
Reweighting the RCT for generalization: finite sample analysis and variable selection
The limited scope of Randomized Controlled Trials (RCT) is increasingly ...
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02/08/2022
Is interpolation benign for random forests?
Statistical wisdom suggests that very complex models, interpolating trai...
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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...
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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 ...
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05/25/2021
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications invo...
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05/13/2021
Generalizing a causal effect: sensitivity analysis and missing covariates
While a randomized controlled trial (RCT) readily measures the average t...
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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...
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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...
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07/03/2020
Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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04/29/2020
Interpretable Random Forests via Rule Extraction
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a...
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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...
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01/13/2020
Trees, forests, and impurity-based variable importance
Tree ensemble methods such as random forests [Breiman, 2001] are very po...
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08/19/2019
SIRUS: making random forests interpretable
State-of-the-art learning algorithms, such as random forests or neural n...
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06/25/2019
AMF: Aggregated Mondrian Forests for Online Learning
Random Forests (RF) is one of the algorithms of choice in many supervise...
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02/19/2019
On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
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03/15/2018
Minimax optimal rates for Mondrian trees and forests
Introduced by Breiman (2001), Random Forests are widely used as classifi...
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11/08/2017
Universal consistency and minimax rates for online Mondrian Forests
We establish the consistency of an algorithm of Mondrian Forests, a rand...
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04/25/2016
Neural Random Forests
Given an ensemble of randomized regression trees, it is possible to rest...
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11/18/2015
A Random Forest Guided Tour
The random forest algorithm, proposed by L. Breiman in 2001, has been ex...
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05/12/2014