6 citations · 6 across the 6 of their papers we have counts for
6 papers
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
Zitao Song, Chao Yang, Chaojie Wang +2
Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool to elicit logic tr…
Neuro-Symbolic Temporal Point Processes
Yang Yang, Chao Yang, Boyang Li +2
Our goal is to discover a compact set of temporal logic rules to explain irregular events of interest. We introduce a neural-symbolic rule induction framewor…
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
Yiling Kuang, Chao Yang, Yang Yang +1
In high-stakes systems such as healthcare, it is critical to understand the causal reasons behind unusual events, such as sudden changes in patient's health. Unveiling the causal r…
Hawkes Processes with Delayed Granger Causality
Chao Yang, Hengyuan Miao, Shuang Li
We aim to explicitly model the delayed Granger causal effects based on multivariate Hawkes processes. The idea is inspired by the fact that a causal event usually takes some time t…
Reinforcement Logic Rule Learning for Temporal Point Processes
Chao Yang, Lu Wang, Kun Gao +1
We propose a framework that can incrementally expand the explanatory temporal logic rule set to explain the occurrence of temporal events. Leveraging the temporal point process mod…
Introspective Tips: Large Language Model for In-Context Decision Making
Liting Chen, Lu Wang, Hang Dong +9
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we em…