AI RESEARCH
Conformalized Signal Temporal Logic Inference under Covariate Shift
arXiv CS.LG
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ArXi:2603.27062v1 Announce Type: new Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent approaches have incorporated conformal prediction as a statistical tool for uncertainty quantification. However, most existing methods rely on the assumption that calibration and testing data are identically distributed and exchangeable, an assumption that is frequently violated in real-world settings.