449 citations · 1.7k across the 108 of their papers we have counts for
42 papers · 1 filter
CatchBench: When Can an Agent Failure Be Caught?
Yue Zhao, Mengyuan Li, Ruolin Li +5
When can an agent failure be caught? A weak audit score alone cannot identify whether the record or the method is limiting. CatchBench therefore puts one auditor's question to thre…
GRADE: Graph Representation of LLM Agent Dependency and Execution
Yue Zhao
Traces record what LLM agents did, leaving implicit what each step relied on. GRADE holds both in one graph; six published graph views are its projections. Execution edges come fre…
DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment
Yi Nian, Tiankai Yang, Yudi Zhang +7
Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically scor…
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
Ojas Nimase, Jiate Li, Yue Zhao +1
Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. However, this transparency cre…
LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning
Haoyan Xu, Ruizhi Qian, Zhengtao Yao +10
Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed…
A Systematic Study of Model Extraction Attacks on Graph Foundation Models
Haoyan Xu, Ruizhi Qian, Jiate Li +9
Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become va…
Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs
Wang Wei, Tiankai Yang, Hongjie Chen +4
Efficient use of large language models (LLMs) is critical for deployment at scale: without adaptive routing, systems either overpay for strong models or risk poor performance from…
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
Langzhou He, Junyou Zhu, Fangxin Wang +5
Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stake…
M3OOD: Automatic Selection of Multimodal OOD Detectors
Yuehan Qin, Li Li, Defu Cao +3
Out-of-distribution (OOD) robustness is a critical challenge for modern machine learning systems, particularly as they increasingly operate in multimodal settings involving inputs…
Graph Synthetic Out-of-Distribution Exposure with Large Language Models
Haoyan Xu, Zhengtao Yao, Ziyi Wang +4
Out-of-distribution (OOD) detection in graphs is critical for ensuring model robustness in open-world and safety-sensitive applications. Existing graph OOD detection approaches typ…
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
Haoyan Xu, Zhengtao Yao, Xuzhi Zhang +6
Out-of-distribution (OOD) detection is critical for ensuring the safety and reliability of machine learning systems, particularly in dynamic and open-world environments. In the vis…
Few-Shot Graph Out-of-Distribution Detection with LLMs
Haoyan Xu, Zhengtao Yao, Yushun Dong +4
Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribu…
ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
Shixuan Li, Wei Yang, Peiyu Zhang +6
Weather forecasting is crucial for public safety, disaster prevention and mitigation, agricultural production, and energy management, with global relevance. Although deep learning…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
Sihan Chen, Zhuangzhuang Qian, Wingchun Siu +10
Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream ana…
DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
Sizhe Liu, Yizhou Lu, Siyu Chen +4
Recent progress in Large Language Models (LLMs) has drawn attention to their potential for accelerating drug discovery. However, a central problem remains: translating theoretical…
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Haoyan Xu, Kay Liu, Zhengtao Yao +4
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to addre…
MetaOOD: Automatic Selection of OOD Detection Models
Yuehan Qin, Yichi Zhang, Yi Nian +2
How can we automatically select an out-of-distribution (OOD) detection model for various underlying tasks? This is crucial for maintaining the reliability of open-world application…
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
Hao Dong, Gaëtan Frusque, Yue Zhao +2
Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial…
ADGym: Design Choices for Deep Anomaly Detection
Minqi Jiang, Chaochuan Hou, Ao Zheng +5
Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of t…
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
Jaemin Yoo, Yue Zhao, Lingxiao Zhao +1
Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cos…
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Xueying Ding, Yue Zhao, Leman Akoglu
Outlier detection (OD) finds many applications with a rich literature of numerous techniques. Deep neural network based OD (DOD) has seen a recent surge of attention thanks to the…
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Peng Xu, Lin Zhang, Xuanzhou Liu +4
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these me…
Weakly Supervised Anomaly Detection: A Survey
Minqi Jiang, Chaochuan Hou, Ao Zheng +6
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.…