943 citations · 1.5k across the 37 of their papers we have counts for
4 papers · 1 filter
A Framework of Transferring Structures Across Large-scale Information Networks
Shan Xue, Jie Lu, Guangquan Zhang +1
The existing domain-specific methods for mining information networks in machine learning aims to represent the nodes of an information network into a vector format. However, the re…
Cross-domain Network Representations
Shan Xue, Jie Lu, Guangquan Zhang
The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning task…
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Zhen Fang, Jie Lu, Feng Liu +2
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- t…
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Feng Liu, Jie Lu, Bo Han +3
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However,…