9 papers
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
Dong Li, Zhengzhang Chen, Xujiang Zhao +5
Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise…
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…
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…