575 citations · 713 across the 9 of their papers we have counts for
9 papers
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
Zicun Cong, Shi Baoxu, Shan Li +3
Fairness in Graph Convolutional Neural Networks (GCNs) becomes a more and more important concern as GCNs are adopted in many crucial applications. Societal biases against sensitive…
Deep Job Understanding at LinkedIn
Shan Li, Baoxu Shi, Jaewon Yang +4
As the world's largest professional network, LinkedIn wants to create economic opportunity for everyone in the global workforce. One of its most critical missions is matching jobs…
Salience and Market-aware Skill Extraction for Job Targeting
Baoxu Shi, Jaewon Yang, Feng Guo +1
At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs Yo…
Learning to Ask Screening Questions for Job Postings
Baoxu Shi, Shan Li, Jaewon Yang +2
At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiri…
Representation Learning in Heterogeneous Professional Social Networks with Ambiguous Social Connections
Baoxu Shi, Jaewon Yang, Tim Weninger +2
Network representations have been shown to improve performance within a variety of tasks, including classification, clustering, and link prediction. However, most models either foc…
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Jaewon Yang, Julian McAuley, Jure Leskovec
Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes…