5 papers
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
Ziyang Cheng, Zhixun Li, Yuhan Li +6
Nowadays, real-world data, including graph-structure data, often arrives in a streaming manner, which means that learning systems need to continuously acquire new knowledge without…
Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
Zhixun Li, Bin Cao, Rui Jiao +9
Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design…
IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification
Zhixun Li, Dingshuo Chen, Tong Zhao +5
Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However,…
Fairness without Demographics through Learning Graph of Gradients
Yingtao Luo, Zhixun Li, Qiang Liu +1
Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data qual…
All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Shuheng Fang, Kangfei Zhao, Yu Rong +2
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations amon…