5 citations · 5 across the 3 of their papers we have counts for
5 papers
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Bowen Hao, Jing Zhang, Cuiping Li +2
The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who ta…
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Bowen Hao, Jing Zhang, Hongzhi Yin +2
Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal…
FLAME: Differentially Private Federated Learning in the Shuffle Model
Ruixuan Liu, Yang Cao, Hong Chen +2
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differenti…
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa +1
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has…
JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment
Bo Chen, Jing Zhang, Xiaobin Tang +2
Abstract. Cross-lingual knowledge alignment is the cornerstone in building a comprehensive knowledge graph (KG), which can benefit various knowledge-driven applications. As the str…