7 papers
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference provides interpretable formulas for time series classification. For multiclass classification, however, accurate predictions do not necessaril…
SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications
Yixuan Wang, Licheng Luo, Yu Fu +3
Translating natural language instructions into machine-interpretable formal specifications enables robots and autonomous systems to plan, reason, and formally verify their behavior…
Quantifying Trade-Offs Between Stability and Goal-Obfuscation
Yixuan Wang, Dan Guralnik, Dan P. Guralnik +2
Safety-critical autonomy in adversarial settings demands more than Lyapunov stability of tracking error signals. An agent executing a goal-directed trajectory is intrinsically legi…
Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective
Yixuan Wang, Wenqian Xue, Warren E. Dixon
Generative models based on static scalar energy functions represent an emerging paradigm in which a single time independent potential drives sample generation through its gradient…
Conformalized Signal Temporal Logic Inference under Covariate Shift
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent appr…
Time-aware Motion Planning in Dynamic Environments with Conformal Prediction
Kaier Liang, Licheng Luo, Yixuan Wang +2
Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning framewo…
Conformal Prediction for Signal Temporal Logic Inference
Danyang Li, Yixuan Wang, Matthew Cleaveland +2
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 rule…