About Lu Yu Lu Yu Ph.D., Computer Science machine learning data mining Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group Events Presented Events Nov 8 - Nov 14, 2020 Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond Lu Yu, Ph.D., Computer Science Nov 11, 16:00 - 18:00 KAUST Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engines and recommendation systems. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information networks through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problems can lead to frequency clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.
Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond Lu Yu, Ph.D., Computer Science Nov 11, 16:00 - 18:00 KAUST Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engines and recommendation systems. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information networks through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problems can lead to frequency clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.
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