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ORCIDLiang Zhao 0002
Person information
- affiliation: Emory University, Atlanta, GA, USA
- affiliation (former): George Mason University, Department of Information Science and Technology, Fairfax, VA, USA
- affiliation (PhD 2016): Virginia Tech, Blacksburg, VA, USA
Other persons with the same name
- Liang Zhao — disambiguation page
- Liang Zhao 0001
![[0000-0002-1502-6604]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-1502-6604")
— University of São Paulo, Institute of Mathematics and Computer Science, SP, Brazil (and 1 more) - Liang Zhao 0003 — University of Technology Sydney, Centre for Autonomous Systems, Faculty of Engineering and Information Technology, NSW, Australia (and 2 more)
- Liang Zhao 0004 — Shenyang Aerospace University, School of Computer Science, China (and 1 more)
- Liang Zhao 0005 — Dalian University of Technology, School of Software Technology, China
- Liang Zhao 0006 — Baidu Research, Institute of Deep Learning, Sunnyvale, CA, USA
- Liang Zhao 0007 — Harbin Institute of Technology, Department of Computer Science and Technology, China
- Liang Zhao 0008 — Huazhong University of Science and Technology, School of Automation, Wuhan, China
- Liang Zhao 0009 — National ICT Australia, Sydney, NSW, Australia (and 1 more)
- Liang Zhao 0010 — Yangtze Normal University, School of Civil and Architectural Engineering, Chongqing, China (and 1 more)
- Liang Zhao 0011 — Beijing Normal University, Faculty of Geographical Science, China
- Liang Zhao 0012 — Guangxi University of Finance and Economics, College of Information and Statistics, Nanning, China
- Liang Zhao 0013 — Kyoto University, Japan
- Liang Zhao 0014 — Central China Normal University, Wuhan, China
- Liang Zhao 0015 — Dalian University of Technology, Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian, China
- Liang Zhao 0016 — Central China Normal University, CCNU, National Engineering Research Center for Educational Big Data, NERC-EBD, Wuhan, China
- Liang Zhao 0017 — Fudan University, School of Basic Medical Sciences, Shanghai, China
- Liang Zhao 0018 — SenseTime Research, Shanghai, China
- Liang Zhao 0019 — Nanjing University, China
- Liang Zhao 0020 — Sichuan University, School of Cyber Science and Engineering, Chengdu, China
- Liang Zhao 0021 — Xidian University, Xi'an, Shaanxi, China
- Liang Zhao 0022 — Macao Polytechnic Institute, Macau, China (and 2 more)
- Liang Zhao 0023 — Xi'an Jiaotong University, Institute of Social Psychology, China (and 1 more)
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2020 – today
- 2024
[j49]Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu:
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks. ACM Comput. Surv. 56(5): 126:1-126:42 (2024)- [j48]Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao:
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. ACM Comput. Surv. 56(7): 188:1-188:39 (2024) - [j47]Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui S. Qin, Liang Zhao:
Controllable Data Generation by Deep Learning: A Review. ACM Comput. Surv. 56(9): 228:1-228:38 (2024) - [j46]Junji Jiang, Chen Ling, Hongyi Li, Guangji Bai, Xujiang Zhao, Liang Zhao:
Quantifying uncertainty in graph neural network explanations. Frontiers Big Data 7 (2024) - [j45]Tanmoy Chowdhury, Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Liang Zhao:
Deep graph representation learning influence maximization with accelerated inference. Neural Networks 180: 106649 (2024) - [j44]Nahyun Kwon, Tong Steven Sun, Yuyang Gao, Liang Zhao, Xu Wang, Jeeeun Kim, Sungsoo Ray Hong:
3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design. Proc. ACM Hum. Comput. Interact. 8(CSCW1): 1-33 (2024) - [j43]Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao:
Toward Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4491-4501 (2024) - [j42]Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, Liang Zhao:
Functional Connectivity Prediction With Deep Learning for Graph Transformation. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4862-4875 (2024) - [c130]Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao:
ELAD: Explanation-Guided Large Language Models Active Distillation. ACL (Findings) 2024: 4463-4475 - [c129]Nguyen Hoang Khoi Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai:
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization. AISTATS 2024: 2296-2304 - [c128]Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao:
Distilling Large Language Models for Text-Attributed Graph Learning. CIKM 2024: 1836-1845 - [c127]Zhiqian Chen, Lei Zhang, Liang Zhao:
Unifying Spectral and Spatial Graph Neural Networks. CIKM 2024: 5511-5513 - [c126]Yifei Zhang, Bo Pan, Siyi Gu, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao:
Visual Attention Prompted Prediction and Learning. IJCAI 2024: 5517-5525 - [c125]Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen:
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning. KDD 2024: 3140-3151 - [c124]Dazhou Yu, Xiaoyun Gong, Yun Li, Meikang Qiu, Liang Zhao:
Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction. KDD 2024: 4001-4011 - [c123]Dazhou Yu, Yuntong Hu, Yun Li, Liang Zhao:
PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph. KDD 2024: 4012-4022 - [c122]Zheng Zhang, Allen Zhang, Ruth Nelson, Giorgio A. Ascoli, Liang Zhao:
Representation Learning of Geometric Trees. KDD 2024: 4374-4385 - [c121]Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao:
Source Localization for Cross Network Information Diffusion. KDD 2024: 5419-5429 - [c120]Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao:
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation. KDD 2024: 6335-6343 - [c119]Zhe Jiang, Liang Zhao, Xun Zhou, Junbo Zhang, Shashi Shekhar, Jieping Ye:
The 4th KDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial'24). KDD 2024: 6722-6723 - [c118]Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen:
Uncertainty Quantification for In-Context Learning of Large Language Models. NAACL-HLT 2024: 3357-3370 - [c117]Zheng Zhang, Liang Zhao:
Self-Similar Graph Neural Network for Hierarchical Graph Learning. SDM 2024: 28-36 - [c116]Dazhou Yu, Binbin Chen, Yun Li, Suman Dhakal, Yifei Zhang, Zhenke Liu, Minxing Zhang, Jie Zhang, Liang Zhao:
STES: A Spatiotemporal Explanation Supervision Framework. SDM 2024: 73-81 - [c115]Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao:
Non-Euclidean Spatial Graph Neural Network. SDM 2024: 154-162 - [c114]Junruo Gao, Chen Ling, Carl Yang, Liang Zhao:
Helper Recommendation with seniority control in Online Health Community. SDM 2024: 253-261 - [e7]Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, A. Taufiq Asyhari, Yonghao Wang:
Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14884, Springer 2024, ISBN 978-981-97-5491-5 [contents] - [e6]Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, A. Taufiq Asyhari, Yonghao Wang:
Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14885, Springer 2024, ISBN 978-981-97-5494-6 [contents] - [e5]Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, A. Taufiq Asyhari, Yonghao Wang:
Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part III. Lecture Notes in Computer Science 14886, Springer 2024, ISBN 978-981-97-5497-7 [contents] - [e4]Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, A. Taufiq Asyhari, Yonghao Wang:
Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 14887, Springer 2024, ISBN 978-981-97-5500-4 [contents] - [e3]Cungeng Cao, Huajun Chen, Liang Zhao, Junaid Arshad, A. Taufiq Asyhari, Yonghao Wang:
Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Birmingham, UK, August 16-18, 2024, Proceedings, Part V. Lecture Notes in Computer Science 14888, Springer 2024, ISBN 978-981-97-5488-5 [contents] - [i112]Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao:
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models. CoRR abs/2401.00625 (2024) - [i111]Jiayu Chang, Shiyu Wang, Chen Ling, Zhaohui Qin, Liang Zhao:
Gene-associated Disease Discovery Powered by Large Language Models. CoRR abs/2401.09490 (2024) - [i110]Nahyun Kwon, Tong Sun, Yuyang Gao, Liang Zhao, Xu Wang, Jeeeun Kim, Sungsoo Ray Hong:
3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration. CoRR abs/2401.15877 (2024) - [i109]Mengdan Zhu, Zhenke Liu, Bo Pan, Abhinav Angirekula, Liang Zhao:
Explaining latent representations of generative models with large multimodal models. CoRR abs/2402.01858 (2024) - [i108]Chen Ling, Xujiang Zhao, Wei Cheng, Yanchi Liu, Yiyou Sun, Xuchao Zhang, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen:
Uncertainty Decomposition and Quantification for In-Context Learning of Large Language Models. CoRR abs/2402.10189 (2024) - [i107]Mingchen Li, Chen Ling, Rui Zhang, Liang Zhao:
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models. CoRR abs/2402.10779 (2024) - [i106]Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao:
Distilling Large Language Models for Text-Attributed Graph Learning. CoRR abs/2402.12022 (2024) - [i105]Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao:
ELAD: Explanation-Guided Large Language Models Active Distillation. CoRR abs/2402.13098 (2024) - [i104]Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai:
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization. CoRR abs/2402.16898 (2024) - [i103]Guangji Bai, Yijiang Li, Chen Ling, Kibaek Kim, Liang Zhao:
Gradient-Free Adaptive Global Pruning for Pre-trained Language Models. CoRR abs/2402.17946 (2024) - [i102]Qilong Zhao, Yifei Zhang, Mengdan Zhu, Siyi Gu, Yuyang Gao, Xiaofeng Yang, Liang Zhao:
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation. CoRR abs/2403.10831 (2024) - [i101]Zheng Zhang, Fan Yang, Ziyan Jiang, Zheng Chen, Zhengyang Zhao, Chengyuan Ma, Liang Zhao, Yang Liu:
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs. CoRR abs/2404.01430 (2024) - [i100]Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao:
Source Localization for Cross Network Information Diffusion. CoRR abs/2404.14668 (2024) - [i99]Zekun Cai, Guangji Bai, Renhe Jiang, Xuan Song, Liang Zhao:
Continuous Temporal Domain Generalization. CoRR abs/2405.16075 (2024) - [i98]Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao:
Network Interdiction Goes Neural. CoRR abs/2405.16409 (2024) - [i97]Yuntong Hu, Zhihan Lei, Zheng Zhang, Bo Pan, Chen Ling, Liang Zhao:
GRAG: Graph Retrieval-Augmented Generation. CoRR abs/2405.16506 (2024) - [i96]Chen Ling, Zhuofeng Li, Yuntong Hu, Zheng Zhang, Zhongyuan Liu, Shuang Zheng, Liang Zhao:
Link Prediction on Textual Edge Graphs. CoRR abs/2405.16606 (2024) - [i95]Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, Liang Zhao:
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations. CoRR abs/2405.16800 (2024) - [i94]Zhuofeng Li, Zixing Gou, Xiangnan Zhang, Zhongyuan Liu, Sirui Li, Yuntong Hu, Chen Ling, Zheng Zhang, Liang Zhao:
TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs. CoRR abs/2406.10310 (2024) - [i93]Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao:
LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multi-modal Foundation Models. CoRR abs/2406.14862 (2024) - [i92]Dazhou Yu, Yuntong Hu, Yun Li, Liang Zhao:
PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph. CoRR abs/2407.00742 (2024) - [i91]Dazhou Yu, Xiaoyun Gong, Yun Li, Meikang Qiu, Liang Zhao:
Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction. CoRR abs/2407.00748 (2024) - [i90]Zheng Zhang, Allen Zhang, Ruth Nelson, Giorgio A. Ascoli, Liang Zhao:
Representation Learning of Geometric Trees. CoRR abs/2408.08799 (2024) - [i89]Zheng Zhang, Hossein Amiri, Dazhou Yu, Yuntong Hu, Liang Zhao, Andreas Züfle:
Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories. CoRR abs/2410.00054 (2024) - [i88]Yuntong Hu, Zhuofeng Li, Zheng Zhang, Chen Ling, Raasikh Kanjiani, Boxin Zhao, Liang Zhao:
HiReview: Hierarchical Taxonomy-Driven Automatic Literature Review Generation. CoRR abs/2410.03761 (2024) - 2023
- [j41]Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao:
Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation. Frontiers Big Data 6 (2023) - [j40]Chen Ling, Carl Yang, Liang Zhao:
Motif-guided heterogeneous graph deep generation. Knowl. Inf. Syst. 65(7): 3099-3124 (2023) - [j39]Tong Steven Sun, Yuyang Gao, Shubham Khaladkar, Sijia Liu, Liang Zhao, Young-Ho Kim, Sungsoo Ray Hong:
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations. Proc. ACM Hum. Comput. Interact. 7(CSCW2): 1-32 (2023) - [j38]Xiaojie Guo, Liang Zhao:
A Systematic Survey on Deep Generative Models for Graph Generation. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 5370-5390 (2023) - [j37]Yuyang Gao, Tanmoy Chowdhury, Lingfei Wu, Liang Zhao:
Modeling Health Stage Development of Patients With Dynamic Attributed Graphs in Online Health Communities. IEEE Trans. Knowl. Data Eng. 35(2): 1831-1843 (2023) - [j36]Xiaojie Guo, Lingfei Wu, Liang Zhao:
Deep Graph Translation. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8225-8234 (2023) - [j35]Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao:
Deep Spatial Prediction via Heterogeneous Multi-source Self-supervision. ACM Trans. Spatial Algorithms Syst. 9(3): 20:1-20:26 (2023) - [c113]Feng Luo, Ling Liu, G. Geoff Wang, Vijay Kumar, Mark S. Ashton, Jacob D. Abernethy, Fatemeh Afghah, Matthew H. E. M. Browning, David Coyle, Philip M. Dames, Tom O'Halloran, James Hays, Patrick Hiesl, Chenfanfu Jiang, Puskar Khanal, Venkat Narayan Krovi, Sara Kuebbing, Nianyi Li, JingJing Liang, Ninghao Liu, Steve McNulty, Christopher M. Oswalt, Neil Pederson, Demetri Terzopoulos, Christopher W. Woodall, Yongkai Wu, Jian Yang, Yin Yang, Liang Zhao:
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI 2023: 1-10 - [c112]Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao:
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates. EMNLP (Findings) 2023: 8035-8047 - [c111]Yifei Zhang, Siyi Gu, Yuyang Gao, Bo Pan, Xiaofeng Yang, Liang Zhao:
MAGI: Multi-Annotated Explanation-Guided Learning. ICCV 2023: 1977-1987 - [c110]Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang:
Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs. ICDE 2023: 1597-1610 - [c109]Lei Zhang, Qisheng Zhang, Zhiqian Chen, Yanshen Sun, Chang-Tien Lu, Liang Zhao:
Infinitely Deep Graph Transformation Networks. ICDM 2023: 778-787 - [c108]Guangji Bai, Chen Ling, Liang Zhao:
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks. ICLR 2023 - [c107]Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Renhao Xue, James Song, Meikang Qiu, Liang Zhao:
Deep Graph Representation Learning and Optimization for Influence Maximization. ICML 2023: 21350-21361 - [c106]Zheng Zhang, Liang Zhao:
Unsupervised Deep Subgraph Anomaly Detection (Extended Abstract). IJCAI 2023: 6514-6518 - [c105]Siyi Gu, Yifei Zhang, Yuyang Gao, Xiaofeng Yang, Liang Zhao:
ESSA: Explanation Iterative Supervision via Saliency-guided Data Augmentation. KDD 2023: 567-576 - [c104]Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo:
Graph Neural Networks: Foundation, Frontiers and Applications. KDD 2023: 5831-5832 - [c103]Zheng Zhang, Junxiang Wang, Liang Zhao:
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First. NeurIPS 2023 - [c102]Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao:
Saliency-Augmented Memory Completion for Continual Learning. SDM 2023: 244-252 - [c101]Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Cristina L. Abad, Carmen Vaca:
Sign-Regularized Multi-Task Learning. SDM 2023: 793-801 - [c100]Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu:
Tutorials at The Web Conference 2023. WWW (Companion Volume) 2023: 648-658 - [i87]Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao:
Knowledge-enhanced Neural Machine Reasoning: A Review. CoRR abs/2302.02093 (2023) - [i86]Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao:
Deep Graph Representation Learning and Optimization for Influence Maximization. CoRR abs/2305.02200 (2023) - [i85]Shiyu Wang, Guangji Bai, Qingyang Zhu, Zhaohui Qin, Liang Zhao:
Domain Generalization Deep Graph Transformation. CoRR abs/2305.11389 (2023) - [i84]Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Carl Yang, Liang Zhao:
Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models. CoRR abs/2305.18703 (2023) - [i83]Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang, Xiaoyun Gong, Liang Zhao:
Graph Neural Network for spatiotemporal data: methods and applications. CoRR abs/2306.00012 (2023) - [i82]Tong Steven Sun, Yuyang Gao, Shubham Khaladkar, Sijia Liu, Liang Zhao, Young-Ho Kim, Sungsoo Ray Hong:
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations. CoRR abs/2307.04036 (2023) - [i81]Guangji Bai, Ziyang Yu, Zheng Chai, Yue Cheng, Liang Zhao:
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction. CoRR abs/2308.13466 (2023) - [i80]Junruo Gao, Chen Ling, Carl Yang, Liang Zhao:
Helper Recommendation with seniority control in Online Health Community. CoRR abs/2309.02978 (2023) - [i79]Chen Ling, Xujiang Zhao, Xuchao Zhang, Yanchi Liu, Wei Cheng, Haoyu Wang, Zhengzhang Chen, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao:
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty. CoRR abs/2309.03433 (2023) - [i78]Xiangru Li, Yifei Zhang, Liang Zhao:
Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation. CoRR abs/2310.02381 (2023) - [i77]Guangji Bai, Qilong Zhao, Xiaoyang Jiang, Yifei Zhang, Liang Zhao:
Saliency-Guided Hidden Associative Replay for Continual Learning. CoRR abs/2310.04334 (2023) - [i76]Zheng Zhang, Hossein Amiri, Zhenke Liu, Andreas Züfle, Liang Zhao:
Large Language Models for Spatial Trajectory Patterns Mining. CoRR abs/2310.04942 (2023) - [i75]Yuntong Hu, Zheng Zhang, Liang Zhao:
Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data. CoRR abs/2310.04944 (2023) - [i74]Zheng Zhang, Chen Zheng, Da Tang, Ke Sun, Yukun Ma, Yingtong Bu, Xun Zhou, Liang Zhao:
Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy. CoRR abs/2310.04945 (2023) - [i73]Zheng Zhang, Liang Zhao:
Transferable Deep Clustering Model. CoRR abs/2310.04946 (2023) - [i72]Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang, Liang Zhao:
Controllable Data Generation Via Iterative Data-Property Mutual Mappings. CoRR abs/2310.07683 (2023) - [i71]Bo Pan, Zhenke Liu, Yifei Zhang, Liang Zhao:
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation. CoRR abs/2310.07698 (2023) - [i70]Yifei Zhang, Siyi Gu, Bo Pan, Guangji Bai, Xiaofeng Yang, Liang Zhao:
Visual Attention-Prompted Prediction and Learning. CoRR abs/2310.08420 (2023) - [i69]Yifei Zhang, Siyi Gu, James Song, Bo Pan, Liang Zhao:
XAI Benchmark for Visual Explanation. CoRR abs/2310.08537 (2023) - [i68]Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao:
Open-ended Commonsense Reasoning with Unrestricted Answer Scope. CoRR abs/2310.11672 (2023) - [i67]Zheng Zhang, Junxiang Wang, Liang Zhao:
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First. CoRR abs/2310.18735 (2023) - [i66]Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao:
Non-Euclidean Spatial Graph Neural Network. CoRR abs/2312.10808 (2023) - [i65]Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen:
Prompt-based Domain Discrimination for Multi-source Time Series Domain Adaptation. CoRR abs/2312.12276 (2023) - 2022
- [j34]Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao:
Small molecule generation via disentangled representation learning. Bioinform. 38(12): 3200-3208 (2022) - [j33]Liang Zhao:
Event Prediction in the Big Data Era: A Systematic Survey. ACM Comput. Surv. 54(5): 94:1-94:37 (2022) - [j32]Junxiang Wang, Hongyi Li, Liang Zhao:
Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Neurocomputing 487: 130-143 (2022) - [j31]Yuyang Gao, Tong Steven Sun, Liang Zhao, Sungsoo Ray Hong:
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. Proc. ACM Hum. Comput. Interact. 6(CSCW2): 1-28 (2022) - [j30]Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Yanfang Ye, Chang-Tien Lu, Naren Ramakrishnan:
Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning. ACM Trans. Knowl. Discov. Data 16(2): 40:1-40:28 (2022) - [j29]Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Online and Distributed Robust Regressions with Extremely Noisy Labels. ACM Trans. Knowl. Discov. Data 16(3): 41:1-41:24 (2022) - [c99]Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao:
Disentangled Spatiotemporal Graph Generative Models. AAAI 2022: 6541-6549 - [c98]Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao:
Adaptive Kernel Graph Neural Network. AAAI 2022: 7051-7058 - [c97]Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho D. Choi:
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph. AAAI 2022: 11538-11546 - [c96]Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao:
From "Dynamics on Graphs" to "Dynamics of Graphs": An Adaptive Echo-State Network Solution (Student Abstract). AAAI 2022: 13111-13112 - [c95]Bo Pan, Yinkai Wang, Xuanyang Lin, Muran Qin, Yuanqi Du, Shiva Ghaemi, Aowei Ding, Shiyu Wang, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Ashley Ann Petersen, Austin Leitgeb, Amarda Shehu, Liang Zhao:
Property-Controllable Generation of Quaternary Ammonium Compounds. BIBM 2022: 3462-3469 - [c94]Yinkai Wang, Shiva Ghaemi, Aowei Ding, Yuanqui Du, Bo Pan, Muran Qin, Xuanyang Lin, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Liang Zhao, Amarda Shehu:
Generation and Characterization of Quaternary Ammonium Compounds via Deep Learning. BIBM 2022: 3512-3519 - [c93]Liming Zhang, Liang Zhao, Dieter Pfoser:
Factorized deep generative models for end-to-end trajectory generation with spatiotemporal validity constraints. SIGSPATIAL/GIS 2022: 59:1-59:12 - [c92]Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao:
Deep geometric neural network for spatial interpolation. SIGSPATIAL/GIS 2022: 72:1-72:4 - [c91]Tanmoy Chowdhury, Ashkan Vakil, Banafsheh Saber Latibari, Sayed Aresh Beheshti-Shirazi, Ali Mirzaeian, Xiaojie Guo, Sai Manoj P. D., Houman Homayoun, Ioannis Savidis, Liang Zhao, Avesta Sasan:
RAPTA: A Hierarchical Representation Learning Solution For Real-Time Prediction of Path-Based Static Timing Analysis. ACM Great Lakes Symposium on VLSI 2022: 493-500 - [c90]Zheng Zhang, Liang Zhao:
Unsupervised Deep Subgraph Anomaly Detection. ICDM 2022: 753-762 - [c89]Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao:
DeepGAR: Deep Graph Learning for Analogical Reasoning. ICDM 2022: 1065-1070 - [c88]Dazhou Yu, Guangji Bai, Yun Li, Liang Zhao:
Deep Spatial Domain Generalization. ICDM 2022: 1293-1298 - [c87]Guangji Bai, Liang Zhao:
Saliency-Regularized Deep Multi-Task Learning. KDD 2022: 15-25 - [c86]Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, Liang Zhao:
RES: A Robust Framework for Guiding Visual Explanation. KDD 2022: 432-442 - [c85]Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. KDD 2022: 1010-1020 - [c84]Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo:
Graph Neural Networks: Foundation, Frontiers and Applications. KDD 2022: 4840-4841 - [c83]Zhe Jiang, Liang Zhao, Xun Zhou, Robert N. Stewart, Junbo Zhang, Shashi Shekhar, Jieping Ye:
DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. KDD 2022: 4878-4879 - [c82]Shiyu Wang, Xiaojie Guo, Liang Zhao:
Deep Generative Model for Periodic Graphs. NeurIPS 2022 - [c81]Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Amarda Shehu, Liang Zhao:
Multi-objective Deep Data Generation with Correlated Property Control. NeurIPS 2022 - [c80]Junxiang Wang, Liang Zhao:
Convergence and Applications of ADMM on the Multi-convex Problems. PAKDD (2) 2022: 30-43 - [c79]Chen Ling, Hengning Cao, Liang Zhao:
STGEN: Deep Continuous-Time Spatiotemporal Graph Generation. ECML/PKDD (3) 2022: 340-356 - [c78]Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao:
Interpretable Molecular Graph Generation via Monotonic Constraints. SDM 2022: 73-81 - [c77]Ya Jiang, Long Jiao, Liang Zhao, Kai Zeng:
Beam Pattern Fingerprinting with Missing Features for Spoofing Attack Detection in Millimeter-Wave Networks. WiseML@WiSec 2022: 75-80 - [c76]Junxiang Wang, Junji Jiang, Liang Zhao:
An Invertible Graph Diffusion Neural Network for Source Localization. WWW 2022: 1058-1069 - [i64]Shiyu Wang, Xiaojie Guo, Liang Zhao:
Deep Generative Model for Periodic Graphs. CoRR abs/2201.11932 (2022) - [i63]Yuyang Gao, Tong Steven Sun, Liang Zhao, Sungsoo Ray Hong:
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. CoRR abs/2202.02838 (2022) - [i62]Mingxuan Ju, Yujie Fan, Yanfang Ye, Liang Zhao:
Black-box Node Injection Attack for Graph Neural Networks. CoRR abs/2202.09389 (2022) - [i61]Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao:
Disentangled Spatiotemporal Graph Generative Models. CoRR abs/2203.00411 (2022) - [i60]Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao:
Interpretable Molecular Graph Generation via Monotonic Constraints. CoRR abs/2203.00412 (2022) - [i59]Guangji Bai, Ling Chen, Liang Zhao:
Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. CoRR abs/2205.10664 (2022) - [i58]Zheng Chai, Guangji Bai, Liang Zhao, Yue Cheng:
Distributed Graph Neural Network Training with Periodic Historical Embedding Synchronization. CoRR abs/2206.00057 (2022) - [i57]Junxiang Wang, Junji Jiang, Liang Zhao:
An Invertible Graph Diffusion Neural Network for Source Localization. CoRR abs/2206.09214 (2022) - [i56]Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. CoRR abs/2206.12327 (2022) - [i55]Chen Ling, Carl Yang, Liang Zhao:
Deep Generation of Heterogeneous Networks. CoRR abs/2206.12336 (2022) - [i54]Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, Liang Zhao:
RES: A Robust Framework for Guiding Visual Explanation. CoRR abs/2206.13413 (2022) - [i53]Guangji Bai, Liang Zhao:
Saliency-Regularized Deep Multi-Task Learning. CoRR abs/2207.01117 (2022) - [i52]Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Liang Zhao:
Controllable Data Generation by Deep Learning: A Review. CoRR abs/2207.09542 (2022) - [i51]Dazhou Yu, Guangji Bai, Yun Li, Liang Zhao:
Deep Spatial Domain Generalization. CoRR abs/2210.00729 (2022) - [i50]Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Amarda Shehu, Liang Zhao:
Multi-objective Deep Data Generation with Correlated Property Control. CoRR abs/2210.01796 (2022) - [i49]Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao:
DeepGAR: Deep Graph Learning for Analogical Reasoning. CoRR abs/2211.10821 (2022) - [i48]Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao:
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. CoRR abs/2212.03954 (2022) - [i47]Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao:
Saliency-Augmented Memory Completion for Continual Learning. CoRR abs/2212.13242 (2022) - 2021
- [j28]Mengchao Xu, Liang Zhao, Ruixin Yang, Jingchao Yang, Dexuan Sha, Chaowei Yang:
Integrating memory-mapping and N-dimensional hash function for fast and efficient grid-based climate data query. Ann. GIS 27(1): 57-69 (2021) - [j27]Xiaosheng Li, Jessica Lin, Liang Zhao:
Time series clustering in linear time complexity. Data Min. Knowl. Discov. 35(6): 2369-2388 (2021) - [j26]Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao:
Deep Graph Learning for Circuit Deobfuscation. Frontiers Big Data 4: 608286 (2021) - [j25]Yuyang Gao, Giorgio A. Ascoli, Liang Zhao:
BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization. Frontiers Neurorobotics 15: 567482 (2021) - [j24]Xiaojie Guo, Liang Zhao, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao:
Deep graph transformation for attributed, directed, and signed networks. Knowl. Inf. Syst. 63(6): 1305-1337 (2021) - [j23]Yuyang Gao, Giorgio A. Ascoli, Liang Zhao:
Schematic memory persistence and transience for efficient and robust continual learning. Neural Networks 144: 49-60 (2021) - [j22]Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sasan, Jessica Lin:
CPM: A general feature dependency pattern mining framework for contrast multivariate time series. Pattern Recognit. 112: 107711 (2021) - [j21]Qingzhe Li, Amir Alipour-Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, Liang Zhao:
Large-scale Cost-Aware Classification Using Feature Computational Dependency Graph. IEEE Trans. Knowl. Data Eng. 33(5): 2029-2044 (2021) - [c75]Negar Etemadyrad, Qingzhe Li, Liang Zhao:
Deep Graph Spectral Evolution Networks for Graph Topological Evolution. AAAI 2021: 7358-7366 - [c74]Yuanqi Du, Yinkai Wang, Fardina Fathmiul Alam, Yuanjie Lu, Xiaojie Guo, Liang Zhao, Amarda Shehu:
Deep Latent-Variable Models for Controllable Molecule Generation. BIBM 2021: 372-375 - [c73]Yuyang Gao, Tong Steven Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Ray Hong, Liang Zhao:
GNES: Learning to Explain Graph Neural Networks. ICDM 2021: 131-140 - [c72]Chen Ling, Carl Yang, Liang Zhao:
Deep Generation of Heterogeneous Networks. ICDM 2021: 379-388 - [c71]Xiaojie Guo, Yuanqi Du, Liang Zhao:
Property Controllable Variational Autoencoder via Invertible Mutual Dependence. ICLR 2021 - [c70]Xiaojie Guo, Yuanqi Du, Liang Zhao:
Deep Generative Models for Spatial Networks. KDD 2021: 505-515 - [c69]Xun Zhou, Liang Zhao, Zhe Jiang, Robert N. Stewart, Shashi Shekhar, Jieping Ye:
DeepSpatial'21: 2nd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. KDD 2021: 4183-4184 - [c68]Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao:
GraphGT: Machine Learning Datasets for Graph Generation and Transformation. NeurIPS Datasets and Benchmarks 2021 - [c67]Zheng Zhan, Liang Zhao:
Representation Learning on Spatial Networks. NeurIPS 2021: 2303-2318 - [c66]Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala:
FedAT: a high-performance and communication-efficient federated learning system with asynchronous tiers. SC 2021: 60 - [c65]Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao:
Disentangled Dynamic Graph Deep Generation. SDM 2021: 738-746 - [c64]Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth A. Loparo, Liang Zhao:
Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. WWW 2021: 518-528 - [c63]Liming Zhang, Liang Zhao, Shan Qin, Dieter Pfoser, Chen Ling:
TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. WWW 2021: 2104-2116 - [d2]Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao:
Dataset for Disentangled Representation Learning for Interpretable Molecule Generation. IEEE DataPort, 2021
- [d1]Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu:
Dataset for Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021. IEEE DataPort, 2021 - [i46]Johnny Torres, Guangji Bai, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina L. Abad:
Sign-regularized Multi-task Learning. CoRR abs/2102.11191 (2021) - [i45]Yuyang Gao, Giorgio A. Ascoli, Liang Zhao:
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. CoRR abs/2105.02085 (2021) - [i44]Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao:
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM framework. CoRR abs/2105.09837 (2021) - [i43]Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu C. Aggarwal, Chang-Tien Lu:
Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks. CoRR abs/2107.10234 (2021) - [i42]Yujie Fan, Mingxuan Ju, Chuxu Zhang, Liang Zhao, Yanfang Ye:
Heterogeneous Temporal Graph Neural Network. CoRR abs/2110.13889 (2021) - [i41]Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho D. Choi:
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph. CoRR abs/2112.00503 (2021) - [i40]Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Liang Zhao, Yanfang Ye:
Adaptive Kernel Graph Neural Network. CoRR abs/2112.04575 (2021) - [i39]Hongyi Li, Junxiang Wang, Yongchao Wang, Yue Cheng, Liang Zhao:
Community-based Layerwise Distributed Training of Graph Convolutional Networks. CoRR abs/2112.09335 (2021) - [i38]Junxiang Wang, Hongyi Li, Liang Zhao:
A Convergent ADMM Framework for Efficient Neural Network Training. CoRR abs/2112.11619 (2021) - [i37]Junxiang Wang, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao:
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages? CoRR abs/2112.12356 (2021) - 2020
- [j20]Sai Manoj Pudukotai Dinakarrao, Xiaojie Guo, Hossein Sayadi, Cameron Nowzari, Avesta Sasan, Setareh Rafatirad, Liang Zhao, Houman Homayoun:
Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics. IEEE Access 8: 138508-138528 (2020) - [j19]Chaowei Yang, Dexuan Sha, Qian Liu, Yun Li, Hai Lan, Weihe Wendy Guan, Tao Hu, Zhenlong Li, Zhiran Zhang, John Hoot Thompson, Zifu Wang, David W. S. Wong, Shiyang Ruan, Manzhu Yu, Douglas Richardson, Luyao Zhang, Ruizhi Hou, You Zhou, Cheng Zhong, Yifei Tian, Fayez Beaini, Kyla Carte, Colin Flynn, Wei Liu, Dieter Pfoser, Shuming Bao, Mei Li, Haoyuan Zhang, Chunbo Liu, Jie Jiang, Shihong Du, Liang Zhao, Mingyue Lu, Lin Li, Huan Zhou, Andrew Ding:
Taking the pulse of COVID-19: a spatiotemporal perspective. Int. J. Digit. Earth 13(10): 1186-1211 (2020) - [j18]Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, Naren Ramakrishnan:
Online flu epidemiological deep modeling on disease contact network. GeoInformatica 24(2): 443-475 (2020) - [j17]Amir Alipour-Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng:
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic. IEEE Trans. Inf. Forensics Secur. 15: 2346-2360 (2020) - [j16]Liang Zhao, Feng Chen, Yanfang Ye:
Efficient Learning with Exponentially-Many Conjunctive Precursors for Interpretable Spatial Event Forecasting. IEEE Trans. Knowl. Data Eng. 32(10): 1923-1935 (2020) - [j15]Qingzhe Li, Liang Zhao, Yi-Ching Lee, Jessica Lin:
Contrast Pattern Mining in Paired Multivariate Time Series of a Controlled Driving Behavior Experiment. ACM Trans. Spatial Algorithms Syst. 6(4): 25:1-25:28 (2020) - [c62]Xuchao Zhang, Xian Wu, Fanglan Chen, Liang Zhao, Chang-Tien Lu:
Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. AAAI 2020: 6853-6860 - [c61]Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Manoj P. D., Houman Homayoun, Liang Zhao:
Estimating the Circuit De-obfuscation Runtime based on Graph Deep Learning. DATE 2020: 358-363 - [c60]Han Wang, Hossein Sayadi, Tinoosh Mohsenin, Liang Zhao, Avesta Sasan, Setareh Rafatirad, Houman Homayoun:
Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. DATE 2020: 1414-1419 - [c59]Yiming Zhang, Yujie Fan, Shifu Hou, Yanfang Ye, Xusheng Xiao, Pan Li, Chuan Shi, Liang Zhao, Shouhuai Xu:
Cyber-guided Deep Neural Network for Malicious Repository Detection in GitHub. ICKG 2020: 458-465 - [c58]Yujie Fan, Yanfang Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, Liang Zhao:
Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. ICDM 2020: 132-141 - [c57]Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao:
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework. ICDM 2020: 591-600 - [c56]Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj P. D., Tinoosh Mohsenin, Khaled N. Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan:
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks. ISQED 2020: 27-32 - [c55]Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye:
Interpretable Deep Graph Generation with Node-edge Co-disentanglement. KDD 2020: 1697-1707 - [i36]Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj P. D., Tinoosh Mohsenin, Khaled N. Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan:
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks. CoRR abs/2001.06099 (2020) - [i35]Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu:
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks. CoRR abs/2002.11867 (2020) - [i34]Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao:
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. CoRR abs/2003.09945 (2020) - [i33]Xiaojie Guo, Sivani Tadepalli, Liang Zhao, Amarda Shehu:
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder. CoRR abs/2004.07119 (2020) - [i32]Liming Zhang, Liang Zhao, Shan Qin, Dieter Pfoser:
TG-GAN: Deep Generative Models for Continuously-time Temporal Graph Generation. CoRR abs/2005.08323 (2020) - [i31]Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye:
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement. CoRR abs/2006.05385 (2020) - [i30]Xiaojie Guo, Liang Zhao:
A Systematic Survey on Deep Generative Models for Graph Generation. CoRR abs/2007.06686 (2020) - [i29]Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao:
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training. CoRR abs/2009.04053 (2020) - [i28]Junxiang Wang, Liang Zhao:
Multi-instance Domain Adaptation for Vaccine Adverse Event Detection. CoRR abs/2009.04901 (2020) - [i27]Liming Zhang, Liang Zhao, Dieter Pfoser:
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints. CoRR abs/2009.09333 (2020) - [i26]Zheng Chai, Yujing Chen, Liang Zhao, Yue Cheng, Huzefa Rangwala:
FedAT: A Communication-Efficient Federated Learning Method with Asynchronous Tiers under Non-IID Data. CoRR abs/2010.05958 (2020) - [i25]Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao:
Disentangled Dynamic Graph Deep Generation. CoRR abs/2010.07276 (2020) - [i24]Wenbin Zhang, Liang Zhao:
Online Decision Trees with Fairness. CoRR abs/2010.08146 (2020)
2010 – 2019
- 2019
- [j14]Song Gao, Shawn D. Newsam, Liang Zhao, Dalton D. Lunga, Yingjie Hu, Bruno Martins, Xun Zhou, Feng Chen:
GeoAI 2019 workshop report: The 3nd ACM SIGSPATIAL International Workshop on GeoAI: AI for Geographic Knowledge Discovery: Seattle, WA, USA - November 5, 2019. ACM SIGSPATIAL Special 11(3): 23-24 (2019) - [j13]Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation. ACM Trans. Knowl. Discov. Data 13(3): 28:1-28:22 (2019) - [j12]Liang Zhao, Olga Gkountouna, Dieter Pfoser:
Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5(3): 19:1-19:28 (2019) - [c54]Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang:
Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting. AAAI 2019: 3638-3646 - [c53]Yanzhi Wang, Zheng Zhan, Liang Zhao, Jian Tang, Siyue Wang, Jiayu Li, Bo Yuan, Wujie Wen, Xue Lin:
Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks. AAAI 2019: 5369-5376 - [c52]Lingwei Chen, Shifu Hou, Yanfang Ye, Thirimachos Bourlai, Shouhuai Xu, Liang Zhao:
iTrustSO: an intelligent system for automatic detection of insecure code snippets in stack overflow. ASONAM 2019: 1097-1104 - [c51]Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao:
Multi-stage Deep Classifier Cascades for Open World Recognition. CIKM 2019: 179-188 - [c50]Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi:
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework. CIKM 2019: 549-558 - [c49]Amir Alipour-Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng:
Machine Learning-Based Delay-Aware UAV Detection Over Encrypted Wi-Fi Traffic. CNS 2019: 1-7 - [c48]Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu:
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction. SIGSPATIAL/GIS 2019: 329-338 - [c47]Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao:
Deep Multi-attributed Graph Translation with Node-Edge Co-Evolution. ICDM 2019: 250-259 - [c46]Yuyang Gao, Lingfei Wu, Houman Homayoun, Liang Zhao:
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. ICDM 2019: 1042-1047 - [c45]Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, Lingfei Wu:
Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. ICDM 2019: 1192-1197 - [c44]Maria Malik, Hassan Ghasemzadeh, Tinoosh Mohsenin, Rosario Cammarota, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad:
ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. ICPP 2019: 7:1-7:11 - [c43]Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu:
iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. IJCAI 2019: 2272-2278 - [c42]Xiaosheng Li, Jessica Lin, Liang Zhao:
Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. IJCAI 2019: 2930-2936 - [c41]Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen:
Interpreting and Evaluating Neural Network Robustness. IJCAI 2019: 4199-4205 - [c40]Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao:
ADMM for Efficient Deep Learning with Global Convergence. KDD 2019: 111-119 - [c39]Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal:
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. KDD 2019: 520-528 - [c38]Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal:
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. KDD 2019: 1418-1428 - [c37]Yue Ning, Liang Zhao, Feng Chen, Chang-Tien Lu, Huzefa Rangwala:
Spatio-temporal Event Forecasting and Precursor Identification. KDD 2019: 3237-3238 - [c36]Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong:
Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network. WWW 2019: 3448-3454 - [e2]Song Gao, Shawn D. Newsam, Liang Zhao, Dalton D. Lunga, Yingjie Hu, Bruno Martins, Xun Zhou, Feng Chen:
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI@SIGSPATIAL 2019, Chicago, IL, USA, November 5, 2019. ACM 2019, ISBN 978-1-4503-6957-2 [contents] - [i23]Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Robust Regression via Online Feature Selection under Adversarial Data Corruption. CoRR abs/1902.01729 (2019) - [i22]Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Sai Manoj P. D., Houman Homayoun, Liang Zhao, Chang-Tien Lu:
Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning. CoRR abs/1902.05357 (2019) - [i21]Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, Bo Yuan:
CircConv: A Structured Convolution with Low Complexity. CoRR abs/1902.11268 (2019) - [i20]Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen:
Interpreting and Evaluating Neural Network Robustness. CoRR abs/1905.04270 (2019) - [i19]Amir Alipour-Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng:
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic. CoRR abs/1905.06396 (2019) - [i18]Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao:
ADMM for Efficient Deep Learning with Global Convergence. CoRR abs/1905.13611 (2019) - [i17]Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad:
Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design. CoRR abs/1907.12952 (2019) - [i16]Yuyang Gao, Lingfei Wu, Houman Homayoun, Liang Zhao:
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. CoRR abs/1908.08497 (2019) - [i15]Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao:
Multi-stage Deep Classifier Cascades for Open World Recognition. CoRR abs/1908.09931 (2019) - [i14]Yuyang Gao, Giorgio A. Ascoli, Liang Zhao:
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization. CoRR abs/1909.13698 (2019) - [i13]Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu:
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction. CoRR abs/1911.08684 (2019) - [i12]Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal:
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. CoRR abs/1911.11119 (2019) - [i11]Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal:
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. CoRR abs/1911.11121 (2019) - 2018
- [j11]Junxiang Wang, Liang Zhao, Yanfang Ye, Yuji Zhang:
Adverse event detection by integrating twitter data and VAERS. J. Biomed. Semant. 9(1): 19:1-19:10 (2018) - [j10]Amr Magdy, Xun Zhou, Liang Zhao, Yan Huang:
2nd ACM SIGSPATIAL workshop on analytics for local events and news (LENS 2018) seattle, washington, USA - November 6, 2018. ACM SIGSPATIAL Special 10(3): 21-22 (2018) - [c35]Yuyang Gao, Liang Zhao:
Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. AAAI 2018: 2999-3006 - [c34]Liang Zhao, Junxiang Wang, Xiaojie Guo:
Distant-Supervision of Heterogeneous Multitask Learning for Social Event Forecasting With Multilingual Indicators. AAAI 2018: 4498-4505 - [c33]Yanfang Ye, Shifu Hou, Lingwei Chen, Xin Li, Liang Zhao, Shouhuai Xu, Jiabin Wang, Qi Xiong:
ICSD: An Automatic System for Insecure Code Snippet Detection in Stack Overflow over Heterogeneous Information Network. ACSAC 2018: 542-552 - [c32]Junxiang Wang, Liang Zhao, Yanfang Ye:
Semi-supervised Multi-instance Interpretable Models for Flu Shot Adverse Event Detection. IEEE BigData 2018: 851-860 - [c31]Lei Zhang, Liang Zhao, Xuchao Zhang, Wenmo Kong, Zitong Sheng, Chang-Tien Lu:
Situation-Based Interpretable Learning for Personality Prediction in Social Media. IEEE BigData 2018: 1554-1562 - [c30]Yuyang Gao, Xiaojie Guo, Liang Zhao:
Local Event Forecasting and Synthesis Using Unpaired Deep Graph Translations. LENS@SIGSPATIAL 2018: 5:1-5:8 - [c29]Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Jiabin Wang, Qi Xiong, Fudong Shao:
KADetector: Automatic Identification of Key Actors in Online Hack Forums Based on Structured Heterogeneous Information Network. ICBK 2018: 154-161 - [c28]Junxiang Wang, Yuyang Gao, Andreas Züfle, Jingyuan Yang, Liang Zhao:
Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. ICDM 2018: 537-546 - [c27]Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Robust Regression via Online Feature Selection Under Adversarial Data Corruption. ICDM 2018: 1440-1445 - [c26]Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu:
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. IJCAI 2018: 3148-3154 - [c25]Ting Hua, Chandan K. Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan:
Social Media based Simulation Models for Understanding Disease Dynamics. IJCAI 2018: 3797-3804 - [c24]Liang Zhao, Amir Alipour-Fanid, Martin Slawski, Kai Zeng:
Prediction-time Efficient Classification Using Feature Computational Dependencies. KDD 2018: 2787-2796 - [c23]Nasrin Akhter, Liang Zhao, Desmond Arias, Huzefa Rangwala, Naren Ramakrishnan:
Forecasting Gang Homicides with Multi-level Multi-task Learning. SBP-BRiMS 2018: 28-37 - [c22]Junxiang Wang, Liang Zhao:
Multi-instance Domain Adaptation for Vaccine Adverse Event Detection. WWW 2018: 97-106 - [e1]Amr Magdy, Xun Zhou, Liang Zhao, Yan Huang:
Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News, SIGSPATIAL 2018, Seattle, WA, USA, November 6, 2018. ACM 2018, ISBN 978-1-4503-6035-7 [contents] - [i10]Yanzhi Wang, Zheng Zhan, Jiayu Li, Jian Tang, Bo Yuan, Liang Zhao, Wujie Wen, Siyue Wang, Xue Lin:
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks. CoRR abs/1803.05391 (2018) - [i9]Xiaojie Guo, Lingfei Wu, Liang Zhao:
Deep Graph Translation. CoRR abs/1805.09980 (2018) - [i8]Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu:
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. CoRR abs/1807.02234 (2018) - [i7]Fuxun Yu, Chenchen Liu, Yanzhi Wang, Liang Zhao, Xiang Chen:
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space. CoRR abs/1810.00144 (2018) - [i6]Zhuwei Qin, Fuxun Yu, Chenchen Liu, Liang Zhao, Xiang Chen:
Interpretable Convolutional Filter Pruning. CoRR abs/1810.07322 (2018) - 2017
- [j9]Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Spatial Event Forecasting in Social Media With Geographically Hierarchical Regularization. Proc. IEEE 105(10): 1953-1970 (2017) - [j8]Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting. IEEE Trans. Knowl. Data Eng. 29(5): 1059-1072 (2017) - [c21]Rupinder Paul Khandpur, Taoran Ji, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik R. Smith, Christopher Adams, Naren Ramakrishnan:
Determining Relative Airport Threats from News and Social Media. AAAI 2017: 4701-4707 - [c20]Xuchao Zhang, Liang Zhao, Zhiqian Chen, Arnold P. Boedihardjo, Jing Dai, Chang-Tien Lu:
Trendi: Tracking stories in news and microblogs via emerging, evolving and fading topics. IEEE BigData 2017: 1590-1599 - [c19]Xuchao Zhang, Zhiqian Chen, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
TRACES: Generating Twitter stories via shared subspace and temporal smoothness. IEEE BigData 2017: 1688-1693 - [c18]Long Hoang Nguyen, Andrew Salopek, Liang Zhao, Fang Jin:
A natural language normalization approach to enhance social media text reasoning. IEEE BigData 2017: 2019-2026 - [c17]Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan:
Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data. CIKM 2017: 507-516 - [c16]Renee Li, Andreas Züfle, Liang Zhao, Georgios Lamprianidis:
Modeling and Prediction of People's Needs (Vision Paper). LENS@SIGSPATIAL 2017: 3:1-3:4 - [c15]Qingzhe Li, Jessica Lin, Liang Zhao, Huzefa Rangwala:
A Uniform Representation for Trajectory Learning Tasks. SIGSPATIAL/GIS 2017: 80:1-80:4 - [c14]Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao:
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks. ICDM 2017: 41-50 - [c13]Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Online and Distributed Robust Regressions Under Adversarial Data Corruption. ICDM 2017: 625-634 - [c12]Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Bo Yuan:
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank. ICML 2017: 4082-4090 - [c11]Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Robust Regression via Heuristic Hard Thresholding. IJCAI 2017: 3434-3440 - [i5]Liang Zhao, Siyu Liao, Yanzhi Wang, Jian Tang, Bo Yuan:
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank. CoRR abs/1703.00144 (2017) - [i4]Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao:
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks. CoRR abs/1709.05246 (2017) - [i3]Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
Online and Distributed Robust Regressions under Adversarial Data Corruption. CoRR abs/1710.00904 (2017) - 2016
- [b1]Liang Zhao:
Spatio-temporal Event Detection and Forecasting in Social Media. Virginia Tech, Blacksburg, VA, USA, 2016 - [j7]Liang Zhao, Ting Hua, Chang-Tien Lu, Ing-Ray Chen:
A topic-focused trust model for Twitter. Comput. Commun. 76: 1-11 (2016) - [j6]Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan:
Automatic targeted-domain spatiotemporal event detection in twitter. GeoInformatica 20(4): 765-795 (2016) - [j5]Yan Shi, Min Deng, Xuexi Yang, Qiliang Liu, Liang Zhao, Chang-Tien Lu:
A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter. ISPRS Int. J. Geo Inf. 5(10): 193 (2016) - [j4]Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Online Spatial Event Forecasting in Microblogs. ACM Trans. Spatial Algorithms Syst. 2(4): 15:1-15:39 (2016) - [c10]Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Multi-resolution Spatial Event Forecasting in Social Media. ICDM 2016: 689-698 - [c9]Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena, Chang-Tien Lu, Anil Vullikanti, Achla Marathe, Kristen Maria Summers, Graham Katz, Andy Doyle, Jaime Arredondo, Dipak K. Gupta, David Mares, Naren Ramakrishnan:
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System. KDD 2016: 205-214 - [c8]Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Hierarchical Incomplete Multi-source Feature Learning for Spatiotemporal Event Forecasting. KDD 2016: 2085-2094 - [i2]Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena, Chang-Tien Lu, Anil Vullikanti, Achla Marathe, Kristen Maria Summers, Graham Katz, Andy Doyle, Jaime Arredondo, Dipak Gupta, David Mares, Naren Ramakrishnan:
EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System. CoRR abs/1604.00033 (2016) - 2015
- [j3]Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
How events unfold: spatiotemporal mining in social media. ACM SIGSPATIAL Special 7(3): 19-25 (2015) - [c7]Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Dynamic theme tracking in Twitter. IEEE BigData 2015: 561-570 - [c6]Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, Naren Ramakrishnan:
SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning. ICDM 2015: 639-648 - [c5]Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Multi-Task Learning for Spatio-Temporal Event Forecasting. KDD 2015: 1503-1512 - [c4]Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan:
Spatiotemporal Event Forecasting in Social Media. SDM 2015: 963-971 - 2014
- [j2]Andy Doyle, Graham Katz, Kristen Maria Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Patrick Butler, Nathan Self, Liang Zhao, Chang-Tien Lu, Rupinder Paul Khandpur, Youssef Fayed, Naren Ramakrishnan:
Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System. Big Data 2(4): 185-195 (2014) - [j1]Fang Jin, Wei Wang, Liang Zhao, Edward R. Dougherty, Yang Cao, Chang-Tien Lu, Naren Ramakrishnan:
Misinformation Propagation in the Age of Twitter. Computer 47(12): 90-94 (2014) - [c3]Andy Doyle, Graham Katz, Kristen Maria Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur, Youssef Fayed, Naren Ramakrishnan:
The EMBERS architecture for streaming predictive analytics. IEEE BigData 2014: 11-13 - [c2]Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Paul Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris J. Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Maria Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares:
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators. KDD 2014: 1799-1808 - [i1]Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Paul Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris J. Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Maria Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares:
'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators. CoRR abs/1402.7035 (2014) - 2013
- [c1]Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan:
STED: semi-supervised targeted-interest event detectionin in twitter. KDD 2013: 1466-1469
Coauthor Index
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