179 citations · 414 across the 12 of their papers we have counts for
12 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…
Visualizing the Flow of Discourse with a Concept Ontology
Baoxu Shi, Tim Weninger
Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sente…
Neural Tensor Factorization
Xian Wu, Baoxu Shi, Yuxiao Dong +2
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their ass…
Open-World Knowledge Graph Completion
Baoxu Shi, Tim Weninger
Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are…
Forward Backward Similarity Search in Knowledge Networks
Baoxu Shi, Lin Yang, Tim Weninger
Similarity search is a fundamental problem in social and knowledge networks like GitHub, DBLP, Wikipedia, etc. Existing network similarity measures are limited because they only co…
ProjE: Embedding Projection for Knowledge Graph Completion
Baoxu Shi, Tim Weninger
With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many…
Scalable Models for Computing Hierarchies in Information Networks
Baoxu Shi, Tim Weninger
Information hierarchies are organizational structures that often used to organize and present large and complex information as well as provide a mechanism for effective human navig…
Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs
Baoxu Shi, Tim Weninger
Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to…