[2603.27062] Conformalized Signal Temporal Logic Inference under Covariate Shift

Computer Science > Machine Learning arXiv:2603.27062 (cs) [Submitted on 28 Mar 2026] Title:Conformalized Signal Temporal Logic Inference under Covariate Shift Authors:Yixuan Wang, Danyang Li, Matthew Cleaveland, Roberto Tron, Mingyu Cai View a PDF of the paper titled Conformalized Signal Temporal Logic Inference under Covariate Shift, by Yixuan Wang and 4 other authors View PDF HTML (experimental) Abstract: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. This paper proposes a conformalized STL inference framework that explicitly addresses covariate shift between training and deployment trajectories dataset. From a technical standpoint, the approach first employs a template-free, differentiable STL inference method to learn an initial model, and subsequently refines it using a limited deployment side dataset to promote distribution alignment. To provide validity guarantees under distribution shift, the framework estimates the likelihood ratio between training and deployment distributions and integrates it into an STL-robustness-based weighted conforma...