WWAggr：一种用于集成变点检测的基于窗口Wasserstein距离的聚合方法
WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
AI总结：
针对高维变点检测中现有单模型效果有限、标准集成聚合方法次优及决策阈值选择难的问题，提出基于Wasserstein距离的任务专属集成聚合方法WWAggr，可适配各类深度CPD模型集成并解决阈值选择难题。
摘要：
变点检测（CPD）旨在识别数据流中分布发生突变的时刻。由于数据模式复杂且常见假设往往不成立，现实世界中的高维变点检测仍具挑战性。当前最先进的检测器依赖独立的深度神经网络，尚未达到完美的检测质量。与此同时，集成学习可提供更鲁棒的解决方案，提升性能。本文研究了深度变点检测器的集成问题，发现标准的预测聚合技术（如取平均）效果次优，且未能考虑该问题的特殊性。为此，我们提出WWAggr——一种基于Wasserstein距离的新型任务专属集成聚合方法。该方法通用性强，可有效适配各类深度CPD模型的集成。此外，与现有方案不同，我们在实际中解决了CPD领域长期存在的决策阈值选择难题。
英文摘要：
Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors and realize that standard prediction aggregation techniques, e.g., averaging, are suboptimal and fail to account for problem peculiarities. Alternatively, we introduce WWAggr -- a novel task-specific method of ensemble aggregation based on the Wasserstein distance. Our procedure is versatile, working effectively with various ensembles of deep CPD models. Moreover, unlike existing solutions, we practically lift a long-standing problem of the decision threshold selection for CPD.
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