让时间序列规则推理有可靠置信度，同时提升准确率与可解释性。
Conformal Prediction for Signal Temporal Logic Inference
- 将置信区间方法融入模型训练，实现端到端可微的逻辑规则学习
- 在基准数据集上减少预测不确定性，覆盖率达95%且集合更小
- 适合需要高可信度规则解释的工业监控、自动驾驶等场景
信号时序逻辑（STL）推理旨在从时序数据中提取人类可理解的规则，但现有方法缺乏对推导规则的形式化置信度保障。共形预测（CP）是一种能提供统计正确性保证的技术，但通常仅作为训练后的后处理，无法改进模型学习。本文提出一种端到端可微的共形预测框架用于STL推理，同时提升结果的可靠性与可解释性。引入基于鲁棒性的非一致性评分，将平滑的共形预测层直接嵌入训练过程，并设计新损失函数，用单一项同时优化推理准确率与共形预测集大小。训练完成后，通过精确的共形预测程序为学习到的STL公式提供统计保证。在基准时序任务上的实验表明，该方法在保持95%以上覆盖率的同时显著缩小预测集，且在固定阈值下误分类数低于当前最优基线。
原文摘要 · Abstract (English)
Signal Temporal Logic (STL) inference seeks to extract human-interpretable rules from time-series data, but existing methods lack formal confidence guarantees for the inferred rules. Conformal prediction (CP) is a technique that can provide statistical correctness guarantees, but is typically applied as a post-training wrapper without improving model learning. Instead, we introduce an end-to-end differentiable CP framework for STL inference that enhances both reliability and interpretability of the resulting formulas. We introduce a robustness-based nonconformity score, embed a smooth CP layer directly into training, and employ a new loss function that simultaneously optimizes inference accuracy and CP prediction sets with a single term. Following training, an exact CP procedure delivers statistical guarantees for the learned STL formulas. Experiments on benchmark time-series tasks show that our approach reduces uncertainty in predictions (i.e., it achieves high coverage while reducing prediction set size), and improves accuracy (i.e., the number of misclassifications when using a fixed threshold) over state-of-the-art baselines.
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