含损坏观测值的保序变点定位与根因分析
Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
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中文总结 AI 辅助
本文针对含损坏观测值的系统，提出加权CONCH与加权CROC方法，结合元学习优化权重，可在保证置信集目标覆盖率的前提下缩小其规模，提升变点定位与根因分析的实用性。
中文摘要 AI 辅助
检测工程系统统计行为发生变化的时间点并识别负责的组件，是电信网络、机器人平台、安全基础设施及多智能体系统监控的核心问题。在安全与关键任务部署场景中，此类决策必须附带统计可靠性保证，而非仅依赖点估计。保序变点定位（CONCH）与保序根因分析（CROC）可满足这一需求，它们返回置信集，以用户指定的概率包含真实变点或真实根因流，且无需对数据生成过程做参数假设。但实际应用中，观测值常被损坏，如出现异常值、传感器故障或对抗扰动。尽管这些方法在受污染情况下仍能保持有限样本覆盖率，但其生成的置信集可能大到失去实际意义。本文采用Huber型污染模型，提出加权CONCH（W-CONCH）与加权CROC（W-CROC），通过对可能损坏的观测值降权，以在数据可能损坏时缩小置信集规模。该加权机制基于未知损坏数据密度的形式化边界推导而来，利用了现有的基于二阶分类器的不确定性信号，如证据深度学习或贝叶斯学习产生的信号。本文还引入元学习过程对权重进行优化，通过优化置信集规模的可微代理函数，进一步推广了W-CONCH与W-CROC。在基于图像的及真实世界的变点与根因基准上的实验表明，基于不确定性的加权方法可在保持目标覆盖率的同时，大幅缩小置信集规模。
英文摘要
Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication networks, robotic platforms, security infrastructure, and multi-agent systems. In safety- and mission-critical deployments, such decisions must be accompanied by statistical reliability guarantees rather than by point estimates alone. Conformal changepoint localization (CONCH) and conformal root cause analysis (CROC) meet this need by returning confidence sets that contain the true changepoint, or the true root-cause stream, with a user-specified probability, without parametric assumptions on the data-generating process. In practice, however, observations are frequently corrupted, e.g., by outliers, sensor faults, or adversarial perturbations. While the finite-sample coverage of these procedures is preserved under contamination, the resulting confidence sets can become uninformatively large. Adopting a Huber-type contamination model, this paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC), which downweight observations that are likely to be corrupted with the goal of reducing confidence set size when data may be corrupted. The weighting mechanism, derived from a formal bound on the unknown corrupted data densities, leverages pre-existing second-order classifier-based uncertainty signals, such as those produced by evidential deep learning or Bayesian learning. W-CONCH and W-CROC are further generalized by introducing a meta-learning procedure for the weights that optimizes a differentiable surrogate of the confidence set size. Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size while maintaining the target coverage.
发表机构
- Korea Advanced Institute of Science and Technology（韩国科学技术院）
- King’s College London（伦敦国王学院）
- Aalborg University（奥尔堡大学）
- Northeastern University London（伦敦东北大学）
机构由 AI 辅助整理，请以论文原文为准。
