7 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…
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…
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…