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

arXiv: https://arxiv.org/abs/2602.03686 · HTML: https://mlsanity.com/paper/2602.03686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data](https://mlsanity.com/paper/2411.10634/) (2024)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)
- [The Effects of Data Quality on Machine Learning Performance on Tabular Data](https://mlsanity.com/paper/2207.14529/) (2022)
- [A Survey on Self-Supervised Learning for Non-Sequential Tabular Data](https://mlsanity.com/paper/2402.01204/) (2024)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies](https://mlsanity.com/paper/2412.18296/) (2024)
- [Robust Learning under Hybrid Noise](https://mlsanity.com/paper/2407.04029/) (2024)
- [Stress-Testing ML Pipelines with Adversarial Data Corruption](https://mlsanity.com/paper/2506.01230/) (2025)

## Abstract (arXiv)

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.