47 citations · 157 across the 25 of their papers we have counts for
14 papers · 1 filter
Non-IID Transfer Learning on Graphs
Jun Wu, Jingrui He, Elizabeth Ainsworth
Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorit…
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen +3
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (w…
Improved Algorithms for Neural Active Learning
Yikun Ban, Yuheng Zhang, Hanghang Tong +2
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce tw…
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Dawei Zhou, Lecheng Zheng, Dongqi Fu +2
Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) witho…
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao, Jun Wu, Jingrui He
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically dis…
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Jun Wu, Jingrui He
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a…