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.

翻译：表格机器学习系统常在受非均匀污染的数据上进行训练，这些污染包括噪声测量、缺失条目和特征特异性偏差。在实践中，这些缺陷通常仅通过列级可靠性指标而非实例级质量标注进行记录，这限制了许多鲁棒性技术和数据清洗技术的适用性。我们提出了QuAIL，一种将特征可靠性先验直接整合到学习过程中的质量感知训练机制。QuAIL通过一个可学习的特征调制层来增强现有模型，该层的更新受到一个依赖于质量的邻近正则化器的选择性约束，从而在不同可信度的特征之间诱导受控的自适应。这能在结构化污染下稳定优化过程，而无需显式数据修复或样本级重加权。在50个分类和回归数据集上的实证评估表明，在随机污染和值相关污染下，QuAIL相较于神经基线模型持续提升了平均性能，在低数据和系统性偏差场景中表现出尤为鲁棒的行为。这些结果表明，将特征可靠性信息直接整合到优化动态中，是实现鲁棒表格学习的一种实用且有效的途径。