Alexis Ayme

A primer on linear classification with missing data

2024 · arXiv (Cornell University)

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 decomposed into $(i)$ impute-then-predict strategies, which first fill …