3 citations · 4 across the 14 of their papers we have counts for
14 papers
LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs
Xiaoxu Ma, Dong Li, Minglai Shao +2
Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction net…
SkillGen: Learning Domain Skills for In-Context Sequential Decision Making
Ruomeng Ding, Wei Cheng, Minglai Shao +1
Large language models (LLMs) are increasingly applied to sequential decision-making through in-context learning (ICL), yet their effectiveness is highly sensitive to prompt quality…
Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models
Zhixia He, Chen Zhao, Minglai Shao +5
Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models…
Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains
Yumeng Lin, Dong Li, Xintao Wu +4
Ensuring fairness and robustness in machine learning models remains a challenge, particularly under domain shifts. We present Face4FairShifts, a large-scale facial image benchmark…
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation
Tao Yin, Chen Zhao, Xiaoyan Liu +1
Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification task…
Hypergraph-Based Dynamic Graph Node Classification
Xiaoxu Ma, Chen Zhao, Minglai Shao +1
Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels chan…
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao +3
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testin…
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models
Zhixia He, Chen Zhao, Minglai Shao +3
Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most exis…
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
Dong Li, Chen Zhao, Minglai Shao +1
Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex c…
FADE: Towards Fairness-aware Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models
Yujie Lin, Dong Li, Minglai Shao +2
Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Tra…
Graphs Generalization under Distribution Shifts
Qin Tian, Wenjun Wang, Chen Zhao +3
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the…
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao +3
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced…
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
Yuanjun Shi, Linzhi Wu, Minglai Shao
Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a…
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
Yujie Lin, Chen Zhao, Minglai Shao +3
Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance o…