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Philip S. Yu

Department of Computer Science, University of Illinois at Chicago

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Oct 25, 2020
Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip S. Yu, Richard Socher, Caiming Xiong

* 19 pages, accepted by EMNLP 2020 main conference as a long paper. Code will be available at https://github.com/salesforce/DNNC-few-shot-intent 
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Basket Recommendation with Multi-Intent Translation Graph Neural Network

Oct 22, 2020
Zhiwei Liu, Xiaohan Li, Ziwei Fan, Stephen Guo, Kannan Achan, Philip S. Yu

* 978-1-7281-6251-5/20/\$31.00~\copyright2020 IEEE 
 * Accepted to IEEE Bigdata 2020. Code is available online at https://github.com/JimLiu96/MITGNN 
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Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

Oct 14, 2020
Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu

* Accepted by ICPR 2020 
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Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

Oct 11, 2020
Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu

* 10 pages, 3 figures. To appear in Findings of EMNLP 2020 
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Mixup-Transfomer: Dynamic Data Augmentation for NLP Tasks

Oct 05, 2020
Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, Lifang He

* Accepted by COLING 2020 
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Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

Oct 05, 2020
He Huang, Shunta Saito, Yuta Kikuchi, Eiichi Matsumoto, Wei Tang, Philip S. Yu

* ACCV 2020 
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KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Sep 26, 2020
Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

* 9 pages, 7 figures 
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Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

Sep 25, 2020
Tao Zhang, Tianqing Zhu, Jing Li, Mengde Han, Wanlei Zhou, Philip S. Yu

* This paper has been published in IEEE Transactions on Knowledge and Data Engineering 
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Fairness Constraints in Semi-supervised Learning

Sep 14, 2020
Tao Zhang, Tianqing Zhu, Mengde Han, Jing Li, Wanlei Zhou, Philip S. Yu


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Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

Sep 04, 2020
Qingyun Sun, Hao Peng, Jianxin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

* accepted by ICDM 2020 as regular paper 
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Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency

Aug 24, 2020
Youwei Liang, Dong Huang, Chang-Dong Wang, Philip S. Yu

* Preprint, under review 
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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

Aug 19, 2020
Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu

* Accepted by CIKM 2020 
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Differentially Private Multi-Agent Planning for Logistic-like Problems

Aug 16, 2020
Dayong Ye, Tianqing Zhu, Sheng Shen, Wanlei Zhou, Philip S. Yu


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Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

Aug 12, 2020
Hao Peng, Jianxin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

* 14 pages, journal 
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Adversarial Directed Graph Embedding

Aug 09, 2020
Shijie Zhu, Jianxin Li, Hao Peng, Senzhang Wang, Philip S. Yu, Lifang He

* 7 pages, 4 figures 
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Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering

Aug 06, 2020
Ye Liu, Shaika Chowdhury, Chenwei Zhang, Cornelia Caragea, Philip S. Yu

* 10 pages, 6 figures 
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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Aug 05, 2020
Tianqing Zhu, Dayong Ye, Wei Wang, Wanlei Zhou, Philip S. Yu


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A Survey on Text Classification: From Shallow to Deep Learning

Aug 04, 2020
Qian Li, Hao Peng, Jianxin Li, Congyin Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He


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A Text Classification Survey: From Shallow to Deep Learning

Aug 02, 2020
Qian Li, Hao Peng, Jianxin Li, Congyin Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He


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LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

Jul 31, 2020
Lichao Sun, Jianwei Qian, Xun Chen, Philip S. Yu


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Network Embedding with Completely-imbalanced Labels

Jul 07, 2020
Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, Philip S. Yu

* A preliminary version of this work was accepted in AAAI 2018. This version has been accepted in IEEE Transactions on Knowledge and Data Engineering (TKDE) 2020. Project page: https://zhengwang100.github.io/project/zero_shot_graph_embedding.html 
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GCN for HIN via Implicit Utilization of Attention and Meta-paths

Jul 06, 2020
Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han


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A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Jul 04, 2020
Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu

* This manuscript was submitted to ACM Computing Surveys 
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Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

Jun 23, 2020
Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

* 10 pages 
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Robust Spammer Detection by Nash Reinforcement Learning

Jun 22, 2020
Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie

* Accepted by KDD 2020 
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Robust Detection of Adaptive Spammers by Nash Reinforcement Learning

Jun 10, 2020
Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie

* 9 pages + 2 pages supplement 
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Forming an Electoral College for a Graph: a Heuristic Semi-supervised Learning Framework

Jun 10, 2020
Chen Li, Xutan Peng, Hao Peng, Jianxin Li, Lihong Wang, Philip S. Yu


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