9 citations · 18 across the 8 of their papers we have counts for
8 papers
GTool: Graph Enhanced Tool Planning with Large Language Model
Wenjie Chen, Wenbin Li, Di Yao +3
Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural l…
CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection
Wenbin Li, Di Yao, Chang Gong +6
Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applica…
Effective and Efficient Representation Learning for Flight Trajectories
Shuo Liu, Wenbin Li, Di Yao +1
Flight trajectory data plays a vital role in the traffic management community, especially for downstream tasks such as trajectory prediction, flight recognition, and anomaly detect…
PORCA: Root Cause Analysis with Partially Observed Data
Chang Gong, Di Yao, Jin Wang +6
Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used…
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
Wenbin Li, Di Yao, Ruibo Zhao +7
The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining. However, current works for evaluating the spatio-tem…
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
Chang Gong, Di Yao, Lei Zhang +4
In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocatio…
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
Shuo Liu, Di Yao, Lanting Fang +5
Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, fina…
Causal Discovery from Temporal Data: An Overview and New Perspectives
Chang Gong, Di Yao, Chuzhe Zhang +2
Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry,…