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On the consistency of supervised learning with missing values

On the consistency of supervised learning with missing values

19 February 2019
Julie Josse
Jacob M. Chen
Nicolas Prost
Erwan Scornet
Gaël Varoquaux
ArXivPDFHTML

Papers citing "On the consistency of supervised learning with missing values"

15 / 15 papers shown
Title
Prediction Models That Learn to Avoid Missing Values
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle
Anton Matsson
Newton Mwai
Fredrik D. Johansson
40
0
0
06 May 2025
Deep learning with missing data
Deep learning with missing data
Tianyi Ma
Tengyao Wang
R. Samworth
59
0
0
21 Apr 2025
Imputation for prediction: beware of diminishing returns
Imputation for prediction: beware of diminishing returns
Marine Le Morvan
Gaël Varoquaux
AI4TS
73
1
0
21 Feb 2025
Robust prediction under missingness shifts
Robust prediction under missingness shifts
P. Rockenschaub
Zhicong Xian
Alireza Zamanian
Marta Piperno
Octavia-Andreea Ciora
E. Pachl
Narges Ahmidi
OOD
42
0
0
24 Jun 2024
Adaptive Optimization for Prediction with Missing Data
Adaptive Optimization for Prediction with Missing Data
Dimitris Bertsimas
A. Delarue
J. Pauphilet
29
1
0
02 Feb 2024
MINTY: Rule-based Models that Minimize the Need for Imputing Features
  with Missing Values
MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values
Lena Stempfle
Fredrik D. Johansson
22
2
0
23 Nov 2023
Algorithmic Recourse with Missing Values
Algorithmic Recourse with Missing Values
Kentaro Kanamori
Takuya Takagi
Ken Kobayashi
Yuichi Ike
21
2
0
28 Apr 2023
The Missing Indicator Method: From Low to High Dimensions
The Missing Indicator Method: From Low to High Dimensions
Mike Van Ness
Tomas M. Bosschieter
Roberto Halpin-Gregorio
Madeleine Udell
AI4TS
19
15
0
16 Nov 2022
Polar Encoding: A Simple Baseline Approach for Classification with
  Missing Values
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values
O. Lenz
Daniel Peralta
Chris Cornelis
21
0
0
04 Oct 2022
PROMISSING: Pruning Missing Values in Neural Networks
PROMISSING: Pruning Missing Values in Neural Networks
S. M. Kia
N. M. Rad
Dan Opstal
B. V. Schie
A. Marquand
J. Pluim
W. Cahn
H. Schnack
VLM
19
4
0
03 Jun 2022
Benchmarking missing-values approaches for predictive models on health
  databases
Benchmarking missing-values approaches for predictive models on health databases
Alexandre Perez-Lebel
Gaël Varoquaux
Marine Le Morvan
Julie Josse
J B Poline
AI4TS
26
35
0
17 Feb 2022
Regression with Missing Data, a Comparison Study of TechniquesBased on
  Random Forests
Regression with Missing Data, a Comparison Study of TechniquesBased on Random Forests
Irving Gómez-Méndez
Émilien Joly
10
13
0
18 Oct 2021
Machine Learning-Based COVID-19 Patients Triage Algorithm using
  Patient-Generated Health Data from Nationwide Multicenter Database
Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database
Min Sue Park
Hyeontae Jo
Haeun Lee
S. Jung
H. Hwang
18
13
0
18 Sep 2021
ranger: A Fast Implementation of Random Forests for High Dimensional
  Data in C++ and R
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Marvin N. Wright
A. Ziegler
93
2,729
0
18 Aug 2015
MissForest - nonparametric missing value imputation for mixed-type data
MissForest - nonparametric missing value imputation for mixed-type data
D. Stekhoven
Peter Buhlmann
157
4,214
0
04 May 2011
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