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Nasim Baharisangari

2 accepted papers

2025

A Logic-Based Approach to Causal Discovery: Signal Temporal Logic Perspective

IJCAI 2025

Causal discovery in time-series datasets is critical for understanding complex systems, especially when the \textit{effectiveness} of causal relationships depends on both the \textit{duration} and \textit{magnitude} of the cause. We introduce a novel framework for causal discovery based on \textbf{S

Cited by 0SourcePDFScholar
2023

Learning Interpretable Temporal Properties from Positive Examples Only

AAAI 2023technical

We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. Following recent research trends, we rely on the fundamental yet interpretable models of deterministic finite automata (DFAs) and linear temporal logic (LTL_f) formulas. In contrast to…