1 citations · 2 across the 5 of their papers we have counts for
6 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…
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…
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…
TLINet: Differentiable Neural Network Temporal Logic Inference
Danyang Li, Mingyu Cai, Cristian-Ioan Vasile +1
There has been a growing interest in extracting formal descriptions of the system behaviors from data. Signal Temporal Logic (STL) is an expressive formal language used to describe…
Multi-class Temporal Logic Neural Networks
Danyang Li, Roberto Tron
Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. The task of binary and multi-class classification for time-series data has…
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control
Wenliang Liu, Danyang Li, Erfan Aasi +3
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods…