25 citations · 35 across the 9 of their papers we have counts for
12 papers
Fix What's Wrong, Keep What's Right: Hallucination Repair for LLM-Based Optimization Modeling
Jiageng Wu, Chao Shen, Zihan Guo +5
Large language models (LLMs) are increasingly being explored to automate operations research modeling, reduce modeling time and lower the expertise barrier to optimization based de…
MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling
Zhong Li, Qi Huang, Yuxuan Zhu +6
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingl…
OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
Zhong Li, Zihan Guo, Xiaohan Lu +5
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference ob…
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang, Zhong Li, Matthijs van Leeuwen +1
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While m…
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
Diffusion and Flow Matching Models for Tabular Data: A Survey
Zhong Li, Qi Huang, Lincen Yang +5
Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, ge…
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Zhong Li, Yuhang Wang, Matthijs van Leeuwen
Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph…
Explainable Graph Neural Networks Under Fire
Zhong Li, Simon Geisler, Yuhang Wang +2
Predictions made by graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. In an attempt to tackl…
Graph Neural Networks based Log Anomaly Detection and Explanation
Zhong Li, Jiayang Shi, Matthijs van Leeuwen
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. Most existing log anomaly detection metho…
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Zhong Li, Matthijs van Leeuwen
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection metho…
A Survey on Explainable Anomaly Detection
Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen
In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding m…
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Zhong Li, Matthijs van Leeuwen
Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection,…