通过最优传输建模分布漂移，实现无标签性能监控与主动标注干预。
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
- 基于最优传输建模渐进式分布漂移，无需标签估算性能变化。
- 在多种缓慢漂移场景下优于现有基线方法，准确率提升显著。
- 量化预测不确定性，指导有限预算下的高效主动标注。
我们研究机器学习模型在渐进式分布漂移下的性能监控问题，即环境随时间缓慢变化，常导致精度下降却难以察觉。为此，我们提出增量式不确定性感知性能监控（IUPM），一种新颖的无标签方法，通过最优传输建模渐进漂移来估计性能变化。同时，IUPM量化性能预测的不确定性，并引入主动标注机制，在有限标注预算下恢复可靠估计。实验表明，IUPM在多种渐进漂移场景中优于现有性能估计基线，其不确定性感知能力比其他策略更有效地引导标签获取。
原文摘要 · Abstract (English)
We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over time, often leading to unnoticed yet significant declines in accuracy. To address this, we propose Incremental Uncertainty-aware Performance Monitoring (IUPM), a novel label-free method that estimates performance changes by modeling gradual shifts using optimal transport. In addition, IUPM quantifies the uncertainty in the performance prediction and introduces an active labeling procedure to restore a reliable estimate under a limited labeling budget. Our experiments show that IUPM outperforms existing performance estimation baselines in various gradual shift scenarios and that its uncertainty awareness guides label acquisition more effectively compared to other strategies.
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