让模型自动关注数据可信度，提升有噪声表格数据的训练效果
QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption
- 在模型中加入可学习的特征调节层，根据特征可靠性动态调整学习过程
- 在50个数据集上测试，对随机和值相关污染均提升平均性能，低数据量下更稳定
- 适合处理带缺失、噪声或偏差的表格数据，尤其适用于缺乏样本级标注场景
表格机器学习系统常在存在非均匀污染的数据上训练，包括噪声测量、缺失值和特征特异性偏差。实践中，这些缺陷通常仅通过列级可靠性指标记录，而非实例级质量标注，限制了多种鲁棒性和清洗技术的应用。我们提出QuAIL，一种将特征可靠性先验直接融入学习过程的质量感知训练机制。QuAIL通过引入一个可学习的特征调制层，其更新受依赖质量的近端正则化约束，从而在不同可信度的特征间实现可控适应。该方法在结构化污染下稳定优化，无需显式数据修复或样本级重加权。在50个分类与回归数据集上的实证评估表明，无论随机还是值相关污染，QuAIL在神经基线基础上持续提升平均性能，尤其在低数据和系统性偏差设置下表现稳健。结果表明，将特征可靠性信息直接嵌入优化动态是实现弹性表格学习的实用且有效途径。
原文摘要 · Abstract (English)
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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