23 citations · 32 across the 5 of their papers we have counts for
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cs.LG2023
Federated Learning over Coupled Graphs
Runze Lei, Pinghui Wang, Junzhou Zhao +6
Graphs are widely used to represent the relations among entities. When one owns the complete data, an entire graph can be easily built, therefore performing analysis on the graph i…
cs.LG2020★ 23 cited
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lin Lan, Pinghui Wang, Xuefeng Du +3
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The n…
cs.LG2019★ 9 cited
Meta Reinforcement Learning with Task Embedding and Shared Policy
Lin Lan, Zhenguo Li, Xiaohong Guan +1
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner…