599 citations · 862 across the 35 of their papers we have counts for
3 papers · 2 filters
A primer on linear classification with missing data
Angel D Reyero Lobo, Alexis Ayme, Claire Boyer +1
Supervised learning with missing data aims at building the best prediction of a target output based on partially-observed inputs. Major approaches to address this problem can be de…
What Is a Good Imputation Under MAR Missingness?
Jeffrey Näf, Erwan Scornet, Julie Josse
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce…