阈值感知的保形路由
Threshold-Aware Conformal Routing
- Rutgers University（罗格斯大学）
- Siemens（西门子）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对高保真模拟与替代模型间的精度-速度权衡，本文提出阈值感知的保形路由（TACR），通过阈值感知目标优化区间分配，在保持无分布覆盖保证的同时，将模拟器调用减少14-75%。
AI中文摘要：
高保真模拟对科学和工程设计至关重要，但反复运行可能代价高昂。学习型替代模型提供了更快的替代方案，但其较高的误差可能改变下游决策。这种精度-速度权衡产生了确定是否可以使用替代模型或仍需完整模拟器的需求。我们研究由标量感兴趣量是否高于或低于固定阈值所决定的决策。对于每个输入，当替代模型的保形区间完全位于阈值一侧时，我们使用替代模型；当区间与阈值相交时，将输入路由至模拟。标准保形预测构造区间时不参考下游决策阈值：即使靠近阈值的窄区间也可能跨越阈值并触发模拟，而远离阈值的较宽区间可以完全位于一侧而无需模拟。我们引入了阈值感知的保形路由（TACR），它使用阈值感知目标学习输入相关的尺度，该目标将区间紧致性集中在决策边界附近。在保留数据上的精确分裂保形校准保持了无分布边际覆盖，这也为未路由的错误阈值决策概率提供了上界。在各种科学和工程数据集上，与相同覆盖目标下的标准保形预测相比，TACR将模拟器延迟调用减少了14-75%。与没有阈值局部加权但具有相似预测器精度的变体相比，TACR在四个数据集上进一步减少了10-24%的延迟调用。这些结果表明，优化用于路由的区间分配可以在不削弱标准保形保证的情况下减少模拟器调用。
英文摘要：
High-fidelity simulations are essential to scientific and engineering design, but can be expensive to run repeatedly. Learned surrogates offer a faster alternative, yet their higher errors may alter downstream decisions. This accuracy-speed tradeoff creates a need to determine whether a surrogate can be used or the full simulator remains necessary. We study decisions determined by whether a scalar quantity of interest lies above or below a fixed threshold. For each input, we use the surrogate when its conformal interval lies entirely on one side of the threshold and route the input to simulation when the interval intersects it. Standard conformal prediction constructs intervals without reference to the downstream decision threshold: even a narrow interval near the threshold can cross it and trigger simulation, whereas a wider interval farther away can remain entirely on one side and require no simulation. We introduce Threshold-Aware Conformal Routing (TACR), which learns an input-dependent scale using a threshold-aware objective that concentrates interval tightness near the decision boundary. Exact split-conformal calibration on held-out data preserves distribution-free marginal coverage, which also upper-bounds the probability of an incorrect threshold decision that is not routed. Across various scientific and engineering datasets, TACR reduces simulator deferrals by 14-75% relative to standard conformal prediction at the same coverage target. Against a variant without threshold-local weighting but with similar predictor accuracy, TACR further reduces deferrals by 10-24% on four datasets. These results show that optimizing interval allocation for routing can reduce simulator calls without weakening the standard conformal guarantee.
