arXiv：cs.LG· Maria V. Salvador, Ana F. Sequeira, Jaime S. Cardoso·· 4 小时前
任务感知插补何时重要？训练-测试不匹配下的鲁棒性分析
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch
AI 导读
研究提出一个将缺失值视为任务条件优化变量的统一框架，并在训练与测试均缺失、仅训练数据不完整两种设定下分别采用摊销方法与隐变量优化方法。在98个表格数据集上评测、36个数据集上评估训练-测试不匹配鲁棒性后发现，重建误差难以代理预测效用（Spearman rho约0.16至0.32），任务感知插补在经典方法失效的场景中胜率最高达80%。
正文
Abstract:Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing approaches treat imputation as a preprocessing step independent of the downstream task, a separation that becomes problematic under train-test mismatches in missingness mechanisms or rates. We introduce a unified framework that treats missing entries as variables in a task-conditioned optimization problem. We instantiate this framework in two settings: an amortized approach for scenarios with missing data at both training and test time, and a latent optimization approach for settings where only training data is incomplete.
The evaluation is conducted on 98 tabular datasets, and robustness under train-test mismatches is assessed on 36 datasets. Results reveal that reconstruction error constitutes a poor proxy for predictive utility (Spearman rho approximately 0.16 to 0.32), and that task-aware imputation becomes critical in scenarios where classical methods fail to recover complete-data performance, achieving win rates of up to 80% in challenging train-test mismatch scenarios.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20342 [cs.LG] |
| (or arXiv:2610.20342v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20342 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Maria Salvador [view email]
[v1]
Wed, 14 Oct 2026 15:22:43 UTC (1,155 KB)
来源：arXiv：cs.LG · arxiv.org