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

TL;DR

QuAIL introduces a novel training method that leverages feature reliability priors to improve robustness of tabular models against data corruption without explicit data repair.

Contribution

It presents a new quality-aware training mechanism that incorporates feature reliability into the learning process through a learnable modulation layer and regularizer.

Findings

Consistently improves performance over neural baselines under corruption.

Robust in low-data and biased settings.

Effective across 50 classification and regression datasets.

Abstract

Tabular machine learning systems are frequently trained on data affected by non-uniform corruption, including noisy measurements, missing entries, and feature-specific biases. In practice, these defects are often documented only through column-level reliability indicators rather than instance-wise quality annotations, limiting the applicability of many robustness and cleaning techniques. We present QuAIL, a quality-informed training mechanism that incorporates feature reliability priors directly into the learning process. QuAIL augments existing models with a learnable feature-modulation layer whose updates are selectively constrained by a quality-dependent proximal regularizer, thereby inducing controlled adaptation across features of varying trustworthiness. This stabilizes optimization under structured corruption without explicit data repair or sample-level reweighting. Empirical evaluation across 50 classification and regression datasets demonstrates that QuAIL consistently improves average performance over neural baselines under both random and value-dependent corruption, with especially robust behavior in low-data and systematically biased settings. These results suggest that incorporating feature reliability information directly into optimization dynamics is a practical and effective approach for resilient tabular learning.