2025
A Unified Framework for Generalization Error Analysis of Learning with Arbitrary Discrete Weak Features
ICML 2025poster
In many real-world applications, predictive tasks inevitably involve low-quality input features (Weak Features; WFs) which arise due to factors such as misobservations, missingness, or partial observations. While several methods have been proposed to estimate the true values of specific types of WFs…