15 citations · 42 across the 13 of their papers we have counts for
5 papers · 1 filter
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…
Heterogeneous Contrastive Learning
Lecheng Zheng, Jinjun Xiong, Yada Zhu +1
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multip…
Generic Outlier Detection in Multi-Armed Bandit
Yikun Ban, Jingrui He
In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthc…
Continuous Transfer Learning with Label-informed Distribution Alignment
Jun Wu, Jingrui He
Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little i…
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn…