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

• URL: http://arxiv.org/abs/2602.03686v1
• Date: Tue, 03 Feb 2026 16:06:30 GMT
• Title: QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption
• Authors: Mattia Sabella, Alberto Archetti, Pietro Pinoli, Matteo Matteucci, Cinzia Cappiello,
• Abstract summary: We present QuAIL, a quality-informed training mechanism that incorporates feature reliability priors directly into the learning process.<n>We show that QuAIL consistently improves average performance over neural baselines under both random and value-dependent corruption.
• Score: 7.630511612007769
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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