QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption

Mattia Sabella, Alberto Archetti, Pietro Pinoli +2

Tabular machine learning systems are frequently trained on data affected by non-uniform corruption, including noisy measurements, missing entries, and feature-specific biases. In p…