Peng CUI

Associate Professor (Tenured)

Lab of Media and Network
Department of Computer Science and Technology
Tsinghua University

Address: Room 9-316, East Main Building, Tsinghua University, Beijing 100084, P.R.China
Tel: +86-10-6279 0810
Fax: +86-10-6277 1138
Email: cuip at tsinghua dot edu dot cn

[ News | Biography | Research Interests | Publications | Honors Awarded | Professional Experiences]
Attention: We have several openings for Postdoctoral Fellowships on causal learning and related topics.
News
Biography

Peng Cui is an Associate Professor with tenure in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include causally-regularized machine learning, network representation learning, and social dynamics modeling. He has published more than 100 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the IEEE Multimedia Best Department Paper Award, SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is PC co-chair of CIKM2019 and MMM2020, SPC or area chair of ICML, KDD, WWW, IJCAI, AAAI, etc., and Associate Editors of IEEE TKDE, IEEE TBD, ACM TIST, and ACM TOMM etc. He received ACM China Rising Star Award in 2015, and CCF-IEEE CS Young Scientist Award in 2018. He is now a Distinguished Member of ACM and CCF, and a Senior Member of IEEE.
 
Selected Publications

Stable Learning and Causal Inference

  • Shaohua Fan, Renzhe Xu, Qian Dong, Yue He, Cheng Chang, Peng Cui. Stable Cox Regression for Survival Analysis under Distribution Shifts. Nature machine intelligence, 2024.(Paper)

  • Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Peng Cui. Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications. ICML, 2024.(Paper)

  • Jose Blanchet, Peng Cui， Jiajin Li, Jiashuo Liu (α-β order). Stability Evaluation via Distributional Perturbation Analysis. ICML, 2024.(Paper)

  • Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui. Domain-wise Data Acquisition to Improve Performance under Distribution Shift. ICML, 2024.(Paper)

  • Han Yu, Jiashuo Liu, Xingxuan Zhang, Jiayun Wu, Peng Cui. A Survey on Evaluation of Out-of-Distribution Generalization. arxiv, 2024.(Paper)

  • Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui. Rethinking the Evaluation Protocol of Domain Generalization. CVPR, 2024.(Paper)

  • Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui. Enhancing Distributional Stability among Sub-populations. AISTATS, 2024.(Paper)

  • Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang. Generalizing graph neural networks on out-of-distribution graphs. IEEE TPAMI, 2023.(Paper)

  • Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong. On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. NeurIPS, 2023.(Paper)

  • Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, Peng Cui. Flatness-Aware Minimization for Domain Generalization. ICCV, 2023.(Paper)

  • Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui. Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. ICML, 2023.(Paper)

  • Xiaoyu Tan, Yong Lin, Shengyu Zhu, Chao Qu, Xihe Qiu, Yinghui Xu, Peng Cui, Yuan Qi. Provably Invariant Learning without Domain Information. ICML, 2023.(Paper)

  • Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Cui, Peng Wu. Propensity Matters: Measuring and Enhancing Balancing for Recommendation. ICML, 2023.(Paper)

  • Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui. Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. KDD, 2023.(Paper)

  • Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui. NICO++: Towards better bechmarks for Out-of-Distribution Generalization. CVPR, 2023.(Paper)

  • Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui. Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization. CVPR, 2023.(Paper)

  • Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Junjian Ye, Peng Cui. Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. CLeaR (Conference on Causal Learning and Reasoning), 2023.(Paper)

  • Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui. Measure the Predictive Heterogeneity. ICLR, 2023.(Paper)

  • Jie Peng, Hao Zou, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei, Peng Cui. Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping. The WebConf, 2023.(Paper)

  • Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang. Stable Learning via Sparse Variable Independence. AAAI, 2023.(Paper)

  • Yue He, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou, Peng Cui. Covariate-Shift Generalization via Random Sample Weighting. AAAI, 2023.(Paper)

  • Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui. Distributionally Robust Optimization with Data Geometry. NeurIPS, 2022.(Paper)

  • Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui. ZIN: When and How to Learn Invariance by Environment Inference? NeurIPS, 2022. (Paper)

  • Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui. Product Ranking for Revenue Maximization with Multiple Purchases. NeurIPS, 2022. (Paper)

  • Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip Yu, Peng Cui. Invariant Preference Learning for General Debiasing in Recommendation. KDD, 2022. (Paper)

  • Renzhe Xu, Zheyan Shen, Xingxuan Zhang, Tong Zhang, Peng Cui. A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. ICML, 2022. (Paper)

  • Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui. Counterfactual Prediction for Outcome-oriented Treatments. ICML, 2022. (Paper)

  • Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang. Model Agnostic Sample Reweighting for Out-of-Distribution Learning. ICML, 2022. (Paper)

  • Peng Cui, Susan Athey. Stable Learning Establishes Some Common Ground Between Causal Inference and Machine Learning. Nature Machine Intelligence, 2022. (Paper)

  • Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, Haoxin Liu. Towards Unsupervised Domain Generalization. CVPR, 2022. (Paper)

  • Linjun Zhou, Peng Cui, Xingxuan Zhang, Yinan Jiang, Shiqiang Yang. Adversarial Eigen Attack on Black-Box Models. CVPR, 2022. (Paper)

  • Renzhe Xu, Xingxuan Zhang, Peng Cui, Bo Li, Zheyan Shen, Jiazheng Xu. Regulatory Instruments for Fair Personalized Pricing. The WebConf, 2022. (Paper)

  • Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang. CausPref: Causal Preference Learning for Out-of-Distribution Recommendation. The WebConf, 2022. (Paper)

  • Renzhe Xu, Peng Cui, Zheyan Shen, Xingxuan Zhang, Tong Zhang. Why Stable Learning Works? A Theory of Covariate Shift Generalization. arXiv, 2021.(Paper)

  • Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen. Kernelized Heterogeneous Risk Minimization. NeurIPS, 2021.(Paper)

  • Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui. Towards Out-Of-Distribution Generalization: A Survey. arxiv, 2021.(Paper)

  • Yue He, Peng Cui, Zheyan Shen, Renzhe Xu, Furui Liu, Yong Jiang. DARING: Differentiable Causal Discovery with Residual Independence. KDD, 2021.(Paper)

  • Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen. Heterogeneous Risk Minimization. ICML, 2021.(Paper)

  • Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen. Deep Stable Learning for Out-Of-Distribution Generalization. CVPR, 2021.(Paper)

  • Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin. Stable Adversarial Learning under Distributional Shifts. AAAI, 2021.(Paper)

  • Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He. Counterfactual Prediction for Bundle Treatments. NeurIPS, 2020.(Paper)

  • Yue He, Zheyan Shen, Peng Cui. Towards Non-I.I.D. Image Classification: A Dataset and Baselines. Pattern Recognition, 2020.(Paper)(Dataset)

  • Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li and Zhitang Chen. Stable Learning via Differentiated Variable Decorrelation. KDD, 2020. (Paper)

  • Yue He, Peng Cui, Jianxin Ma, Hao Zou, Xiaowei Wang, Hongxia Yang and Philip S. Yu. Learning Stable Graphs from Multiple Environments with Selection Bias. KDD, 2020. (Paper)

  • Renzhe Xu, Peng Cui, Kun Kuang, Bo Li, Linjun Zhou, Zheyan Shen and Wei Cui. Algorithmic Decision Making with Conditional Fairness. KDD, 2020. (Paper)

  • Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang. Stable Learning via Sample Reweighting. AAAI, 2020.(Paper)

  • Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li. Stable Prediction with Model Misspecification and Agnostic Distribution Shift. AAAI, 2020.(Paper)

  • Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Yashen Wang, Fei Wu, Shiqiang Yang. Treatment Effect Estimation via Differentiated Confounder Balancing and Regression. ACM TKDD, 2019.(Paper)

  • Hao Zou, Kun Kuang, Boqi Chen, Peng Cui, Peixuan Chen. Focused Context Balancing for Robust Offline Policy Evaluation. KDD, 2019.(Paper)

  • Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Xiong and Bo Li. Stable Prediction across Unknown Environments. KDD, 2018.(Paper)

  • Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, Peixuan Chen. Causally Regularized Learning with Agnostic Data Selection Bias. ACM Multimedia, 2018. (Paper)

  • Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang. Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing. KDD, 2017. (Oral) (Download)

  • Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Fei Wang, Shiqiang Yang. Treatment Effect Estimation with Data-Driven Variable Decomposition. AAAI, 2017. (Download)

  • Kun Kuang, Meng Jiang, Peng Cui, Shiqiang Yang. Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach. IEEE Transactions on Big Data, 2017. (Download)

  • Kun Kuang, Meng Jiang, Peng Cui, Shiqiang Yang. Steering Social Media Promotions with Effective Strategies. ICDM, 2016. (Short Paper) (Download)


Network Embedding

  • Daixin Wang, Zujian Weng, Zhengwei Wu, Zhiqiang Zhang, Peng Cui, Hongwei Zhao, Jun Zhou. A Graph Learning Based Framework for Billion-Scale Offline User Identification. KDD, 2022. (to appear)

  • Ziwei Zhang, Chenhao Niu, Peng Cui, Bo Zhang, Wei Cui, Wenwu Zhu. A Simple and General Graph Neural Network with Stochastic Message Passing. arxiv, 2021. (Paper)

  • Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu. Signed Graph Neural Network with Latent Groups. KDD, 2021. (Paper)

  • Yue He, Yancheng Dong, Peng Cui, Yuhang Jiao, Xiaowei Wang, Ji Liu, Philip Yu. Purify and Generate: Learning Faithful Item-to-Item Graph from Noisy User-Item Interaction Behaviors. KDD, 2021. (Paper)

  • Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi and Jian Pei. AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. KDD, 2020. (Paper)

  • Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang and Wenwu Zhu. Disentangled Self-Supervision in Sequential Recommenders. KDD, 2020. (Paper)

  • Xiao Wang, Yuanfu Lu, Chuan Shi, Ruijia Wang, Peng Cui, Shuai Mou. Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity. IEEE TKDE, 2020. (Paper)

  • Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu. Learning Disentangled Representations for Recommendation. NeurIPS, 2019. (Paper)

  • Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang. A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. AAAI, 2020. (Paper)

  • Guanglin Niu, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, Xiaowei Zhang. Rule-Guided Compositional Representation Learning on Knowledge Graphs. AAAI, 2020. (Paper)

  • Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Qi Yuan. A Semi-supervised Graph Attentive Network for Fraud Detection. ICDM, 2019. (Paper)

  • Ke Tu, Jianxin Ma, Peng Cui, Jian Pei, Wenwu Zhu. AutoNE: Hyperparameter Optimization for Massive Network Representation Learning. KDD, 2019. (Paper)

  • Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu. Robust Graph Convolutional Networks Against Adversarial Attacks. KDD, 2019. (Paper)

  • Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu. Disentangled Graph Convolutional Networks. ICML, 2019. (Paper)

  • Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu. A Survey on Network Embedding. IEEE TKDE, 2018. (Paper)

  • Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, Philip S. Yu. Heterogeneous Graph Attention Network. WWW, 2019. (Paper)

  • Di Jin, Xinxin You, Weihao Li, Dongxiao He, Peng Cui, Francoise Fogelman-Soulie, Tanmoy Chakraborty. Incorporating Network Embedding into Markov Random Field for Better Community Detection. AAAI, 2019. (Paper)

  • Chang Su, Jie Tong, Yongjun Zhu, Peng Cui, Fei Wang. Network Embedding in Biomedical Data Science. Briefings in Bioinformatics, 2018. (Paper)

  • Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu. Billion-scale Network Embedding with Iterative Random Projection. IEEE ICDM, 2018. (Paper)

  • Dingyuan Zhu, Peng Cui, Daixin Wang and Wenwu Zhu. Deep Variational Network Embedding in Wasserstein Space. KDD, 2018.(Paper)

  • Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao and Wenwu Zhu. Arbitrary-Order Proximity Preserved Network Embedding. KDD, 2018.(Paper)

  • Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu and Wenwu Zhu. Deep Recursive Network Embedding with Regular Equivalence. KDD, 2018.(Paper)

  • Jianxin Ma, Peng Cui, Xiao Wang and Wenwu Zhu. Hierarchical Taxonomy Aware Network Embedding. KDD, 2018.(Paper)

  • Xumin Chen, Peng Cui, Lingling Yi, Shiqiang Yang. Scalable Optimization for Embedding Highly-Dynamic and Recency-Sensitive Data. KDD, 2018.(Applied Data Science track)(Paper)

  • Dingyuan Zhu, Peng Cui, Ziwei Zhang, Jian Pei, Wenwu Zhu. High-order Proximity Preserved Embedding For Dynamic Networks. IEEE TKDE, 2018. (Paper)

  • Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu. TIMERS: Error-Bounded SVD Restart on Dynamic Networks. AAAI, 2018. (Paper)

  • Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu. Structural Deep Embedding for Hyper-Networks. AAAI, 2018. (Paper)

  • Jianxin Ma, Peng Cui, Wenwu Zhu. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI, 2018. (Paper)

  • Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang. Community Preserving Network Embedding. AAAI, 2017. (Paper) (Soucecode)

  • Daixin Wang, Peng Cui, Wenwu Zhu. Structural Deep Network Embedding. KDD, 2016. (Full Paper, Oral) (Paper) (Soucecode)

  • Mingdong Ou, Peng Cui, Jian Pei, Wenwu Zhu. Asymmetric Transitivity Preserving Graph Embedding. KDD, 2016. (Full Paper, Oral) (Paper) (Soucecode)

  • Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu. Non-transitive Hashing with Latent Similarity Components. KDD, 2015.(Full Paper, Oral)(Download)

  • Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu, Shiqiang Yang. Comparing Apples to Oranges: A Scalable Solution with Heterogeneous Hashing. KDD, 2013.(Full Paper, Oral)(Download)


Social Dynamics Modeling

  • Yunfei Lu, Peng Cui, Linyun Yu, Lei Li, Wenwu Zhu. Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture Model KDD, 2022. (to appear)

  • Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, Yishi Lin. Fates of Microscopic Social Ecosystems: Keep Alive or Dead? KDD, 2019.(Paper)

  • Chengxi Zang, Peng Cui, Wenwu Zhu, Fei Wang. Dynamical Origins of Distribution Functions. KDD, 2019.(Paper)

  • Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu, Fei Wang. Uncovering Pattern Formation of Information Flow. KDD, 2019.(Paper)

  • Yunfei Lu, Linyun Yu, Peng Cui, Chengxi Zang, Renzhe Xu, Yihao Liu, Lei Li, Wenwu Zhu. Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena. KDD, 2019.(Applied Data Science Track)(Paper)

  • Jianrong Tao, Jianshi Lin, Shize Zhang, Sha Zhao, Runze Wu, Changjie Fan, Peng Cui. MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games. KDD, 2019.(Applied Data Science Track)(Paper)

  • Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu. Collective Human Behavior in Cascading System: Discovery, Modeling and Applications. IEEE ICDM, 2018. (Paper)

  • Chengxi Zang, Peng Cui, Wenwu Zhu. Learning and Interpreting Complex Distributions in Empirical Data. KDD, 2018.(Paper)

  • Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu. On Power Law Growth of Social Networks. IEEE TKDE, 2018. (Download)

  • Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu. Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity. KDD, 2017. (Oral) (Download)

  • Linyun Yu, Peng Cui, Chaoming Song, Tianyang Zhang, Shiqiang Yang. A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics. KDD, 2017. (Poster) (Download)

  • Tianyang Zhang, Peng Cui, Christos Faloutsos, Yunfei Lu, Wenwu Zhu, Shiqiang Yang. comeNgo: A Dynamic Model for Social Group Evolution. ACM TKDD, 2017. (Download)

  • Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, Shiqiang Yang. Uncovering and Predicting the Dynamic Process of Information Cascades with Survival Model. Knowledge and Information Systems, 2016. (Download)

  • Tianyang Zhang, Peng Cui, Christos Faloutsos, Wenwu Zhu, Shiqiang Yang. Come-and-Go Patterns of Group Evolution: A Dynamic Model. KDD, 2016. (KDD 2016 Best Paper Finalist) (Download)

  • Chengxi Zang, Peng Cui, Christos Faloutsos. Beyond Sigmoids: the NetTide Model for Social Network Growth, and its Applications. KDD, 2016. (Full Paper, Poster) (Download)

  • Meng Jiang, Alex Beutel, Peng Cui, Bryn Hooi, Shiqiang Yang, Christos Faloutsos. Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algortihms. IEEE TKDE, 2016. (Download)

  • Tianyang Zhang, Peng Cui, Chaoming Song, Wenwu Zhu, Shiqiang Yang. A Multiscale Survival Process for Modeling Human Activity Patterns. PLOS ONE, 2016. (Download)

  • Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, Shiqiang Yang. From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics. IEEE ICDM, 2015. (IEEE ICDM2015 Best Student Paper Award)(Download)


Social Recommendation and Behavioral Modeling

  • Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang. Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds. AAAI, 2016. (Download)

  • Meng Jiang, Peng Cui, Christos Faloutsos. Suspicious Behavior Detection: Current Trends and Future Directions. IEEE Intelligent Systems, 2015. (Download)

  • Meng Jiang, Alex Beutel, Peng Cui, Bryn Hooi, Shiqiang Yang, Christos Faloutsos. A General Suspiciousness Metric for Dense Blocks in Multimodal Data. IEEE ICDM, 2015.(Short Paper) (Download)

  • Meng Jiang, Peng Cui, Alex Beutel, Wenwu Zhu, Shiqiang Yang. Inferring Lockstep Behavior from Connectivity Pattern in Large Graphs. Knowledge and Information Systems (KAIS), 2015.(to appear)(Download)

  • Meng Jiang, Peng Cui, Fei Wang, Wenwu Zhu, Shiqiang Yang. Social Recommendation with Cross-Domain Transferable Knowledge. IEEE TKDE, 2015.(to appear)(Download)

  • Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang. Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach. ACM Transactions on Knowledge Discovery from Data (TKDD), 2015.(to appear) (special issue on The Best of KDD2014)(Download)

  • Peng Cui, Tianyang Zhang, Fei Wang, Peng He. Perceiving Group Themes from Collective Social and Behavioral Information. AAAI, 2015.(to appear) (Download)

  • Mingdong Ou, Peng Cui, Jun Wang, Fei Wang, Wenwu Zhu. Probabilistic Attributed Hashing. AAAI, 2015.(to appear) (Download)

  • Meng Jiang, Peng Cui, Fei Wang, Wenwu Zhu, Shiqiang Yang. Scalable Recommendation with Social Contextual Information. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2014.(to appear) (Download)

  • Meng Jiang, Peng Cui, Alex Beutel,Christos Faloutsos, Shiqiang Yang. CatchSync: Catching Synchronized Behavior in Large Directed Graphs. KDD, 2014. (Best Paper Finalist) (Download)

  • Meng Jiang, Peng Cui, Fei Wang, Xinran Xu, Wenwu Zhu, Shiqiang Yang. FEMA: Flexible Evolutionary Multi-faceted Analysis for Dynamic Behavioral Pattern Discovery. KDD, 2014.(Full Paper, accepting rate 14.8%) (Download)

  • Meng Jiang, Peng Cui, Alex Beutel,Christos Faloutsos, Shiqiang Yang. Detecting Suspicious Following Behavior in Multimillion-Node Social Networks. WWW, 2014.(Poster)(Download)

  • Meng Jiang, Peng Cui, Alex Beutel,Christos Faloutsos, Shiqiang Yang. Inferring Strange Behavior from Connectivity Pattern in Social Networks. PAKDD, 2014.(Full Paper, accepting rate 10.8%)(Download)

  • Peng Cui, Shifei Jin, Linyun Yu, Fei Wang, Wenwu Zhu, Shiqiang Yang. Cascading Outbreak Prediction in Networks: A Data-Driven Approach. ACM SIGKDD, 2013.(Full Paper, accepting rate 17.4%)(Download)

  • Zhi Wang, Wenwu Zhu, Peng Cui, Lifeng Sun, Shiqiang Yang. Social Media Recommendation. In Social Media Retrieval, Naeem Ramzan, Roelof van Zwol, Jong‐Seok Lee, Kai Clüver, and Xian‐Sheng Hua, Editors, Springer-Verlag, ISBN 978-1-4471-4554-7, 2013 .

  • Meng Jiang, Peng Cui, Fei Wang, Qiang Yang, Wenwu Zhu, Shiqiang Yang. Social Recommendation Across Multiple Relational Domains. ACM CIKM, 2012.(Full Paper, accepting rate 13%)(Download)

  • Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, Shiqiang Yang. Social Contextual Recommendation. ACM CIKM, 2012.(Full Paper, accepting rate 13%)(Download)

  • Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang. Who Should Share What? Item-level Social Influence Prediction for Users and Posts Ranking. International ACM SIGIR Conference (SIGIR), 2011.(Full paper, accepting rate 19.8% )(Download)

  • Peng Cui, Fei Wang, Shiqiang Yang. Item-Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization. Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2011.(Oral, accepting rate 24.8% )

  • Fei Wang, Peng Cui, Gordon Sun, Tat-Seng Chua, Shiqiang Yang. Guest editorial: Special issue on information retrieval for social media. Information Retrieval, vol. 15, no. 3-4, pp. 179-182, 2012.


Social-Sensed Multimedia Computing

  • Linjun Zhou, Peng Cui, Xu Jia, Shiqiang Yang, Qi Tian. Learning to Select Base Classes for Few-shot Classification. CVPR, 2020. (Download)

  • Linjun Zhou, Peng Cui, Wenwu Zhu, Shiqiang Yang, Qi Tian. Learning to Learn Image Classifiers with Visual Analogy. CVPR, 2019. (Download)

  • Daixin Wang, Peng Cui, Wenwu Zhu. Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation. AAAI, 2018. (Download)

  • Peng Cui, Shaowei Liu, Wenwu Zhu. General Knowledge Embedded Image Representation Learning. IEEE Transactions on Multimedia, 2017. (Download)

  • Peng Cui, Wenwu Zhu, Tat-Seng Chua, Ramesh Jain. Social-Sensed Multimedia Computing. IEEE Multimedia, 2016. (IEEE Multimedia Best Department Paper Award)(Download)

  • Shaowei Liu, Peng Cui, Wenwu Zhu, Shiqiang Yang. Learning Socially Embedded Visual Representation from Scratch. ACM Multimedia, 2015.(to appear) (Download)

  • Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu. Learning Compact Hash Codes for Multimodal Representations using Orthogonal Deep Structure. IEEE Transactions on Multimedia, 2015.(to appear) (Download)

  • Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu. Deep Multimodal Hashing with Orthogonal Regularization. IJCAI, 2015.(to appear) (Download)

  • Peng Cui, Shaowei Liu, Wenwu Zhu, Huanbo Luan, Tat-Seng Chua, Shiqiang Yang. Social-Sensed Image Search. ACM Transactions on Information System (TOIS), 2014.(to appear)

  • Zhiyu Wang, Peng Cui, Fangtao Li, Edward Y Chang, Shiqiang Yang. A Data-Driven Study of Image Feature Extraction and Fusion. Information Science, 2014.(to appear) (Download)

  • Shaowei Liu, Peng Cui, Huanbo Luan, Wenwu Zhu, Shiqiang Yang, Qi Tian. Social-Oriented Visual Image Search. Computer Vision and Image Understanding (CVIU), vol. 118, pp. 30-39, 2014.(Download)

  • Dan Xu, Peng Cui, Wenwu Zhu, Shiqiang Yang. Graph-Based Residence Location Inference for Social Media Users. IEEE MultiMedia, vol.21, no. 4, pp. 76-83, Oct.-Dec. 2014.(Download)

  • Zhiyu Wang, Peng Cui, Lexing Xie, Wenwu Zhu, Yong Rui, Shiqiang Yang. Bilateral Correspondence Model for Words-and-Pictures Association in Media-rich Microblogs. ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), 2014.(Download)

  • Peng Cui, Zhiyu Wang, Zhou Su. What Videos Are Similar With You? Learning a Common Attributed Representation for Video Recommendation. ACM Multimedia, 2014.(Full Paper)(Download)

  • Shaowei Liu, Peng Cui, Wenwu Zhu, Shiqiang Yang, Qi Tian. Social Embedding Image Distance Learning. ACM Multimedia, 2014.(Full Paper)(Download)

  • Dan Xu, Peng Cui, Wenwu Zhu, Shiqiang Yang. Find You from Your Friends: Graph-based Residence Location Prediction for Users in Social Media. IEEE ICME, 2014. (Best Paper Award) (Download)

  • Yun Yang, Peng Cui, Vicky Zhao, Wenwu Zhu, Shiqiang Yang. Emotionally Representative Image Discovery for Social Events. ACM ICMR, 2014.(Full Paper)(Download)

  • Yun Yang, Peng Cui, Wenwu Zhu, Shiqiang Yang. User Interest and Social Influence Based Emotion Prediction for Individuals. ACM Multimedia, 2013.(Short Paper)(Download)

  • Tao Chen, Dongyuan Lu, Min-Yen Kan, Peng Cui. Understanding and Classifying Image Tweets. ACM Multimedia, 2013.(Short Paper)(Download)

  • Shaowei Liu, Peng Cui, Huanbo Luan, Wenwu Zhu, Shiqiang Yang, Qi Tian. Social Visual Image Ranking for Web Image Search. International Conference on Multimedia Modeling (MMM), 2013.(Oral Paper, accepting rate 27%)(Best Paper Award)(Download)

  • Zhi Wang, Wenwu Zhu, Xiangwen Chen, Lifeng Sun, Jiangchuan Liu, Minghua Chen, Peng Cui, Shiqiang Yang. Propagation-Based Social-Aware Multimedia Content Distribution. ACM Transactions on Multimedia Computing Communications and Applications (TOMCCAP), 2013.(to appear)

  • Zhiyu Wang, Peng Cui, Lexing Xie, Hao Chen, Wenwu Zhu, Shiqiang Yang. Analyzing Social Media via Event Facets. ACM Multimedia, 2012.(Grand Challenge Multimodal Award)


Event Mining from Surveillance Videos

  • Peng Cui, Fei Wang, Lifeng Sun, Shiqiang Yang. A Matrix-Based Approach to Unsupervised Human Action Categorization. IEEE Transactions on Multimedia (TMM), vol. 14, no. 1, pp. 102-110, 2012.(SCI IF=2.29 )

  • Chao Wang, Lifeng Sun, Peng Cui, Jianwei Zhang, Shiqiang Yang. Analyzing Image Deblurring Through Three Paradigms. IEEE Transactions on Image Processing (TIP), vol. 21, no. 1, pp.115-129, 2012.(SCI IF=2.6)

  • Peng Cui, Zhiqiang Liu, Lifeng Sun, Shiqiang Yang. Hierarchical Visual Event Pattern Mining and Its Applications. Data Mining and Knowledge Discovery (DMKD), vol. 22, no. 3, pp. 467-492, 2011.(SCI IF=2.95 )(Download)

  • Yinjun Miao, Chao Wang, Peng Cui, Lifeng Sun, Shiqiang Yang. HFAG: Hierarchical Frame Affinity Group for Video Retrieval on Very Large Dataset. IEEE International Conference on Image Processing, 2010.

  • Peng Cui, Lifeng Sun, Fei Wang, Shiqiang Yang. Contextual Mixture Tracking. IEEE Transactions on Multimedia (TMM), vol. 11, pp. 333-341, 2009.(SCI IF=2.29 )(Download)

  • Peng Cui, Lifeng Sun, Shiqiang Yang. Adaptive Mixture Observation Models for Multi-Object Tracking. Science in China Series F: Information Sciences, vol. 52, no. 2, pp. 226-235, 2009.

  • Peng Cui, Fei Wang, Lifeng Sun, Shiqiang Yang. A Joint Matrix Factorization Approach to Unsupervised Action Categorization. IEEE International Conference on Data Mining (ICDM), 2008.(Download)

  • Peng Cui, Lifeng Sun, Zhiqiang Liu, Shiqiang Yang. A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events. Workshop on Visual Surveillance, in conjuction with CVPR, 2007.(Download)

  • Peng Cui, Lifeng Sun, Zhi Wang, Shiqiang Yang. A Novel Segment-of-Interest Discovery Method for Surveillance Video. IEEE International Conference on Multimedia and Expo (ICME), 2007.(Download)

Honors Awarded
 
2020 ACM Distinguished Member
2020 CCF Distinguished Member
2018 CCF-IEEE CS Young Scientist Award
2018 IEEE Multimedia Best Department Paper Award
2016 KDD 2016 Best Paper Finalist
2016 Young Talent Program of China Association for Science and Technology
2015 ACM China Rising Star Award
2015 IEEE ICDM 2015 Best Student Paper Award
2014 ACM SIGKDD 2014 Best Paper Finalist
2014 IEEE ICME 2014 Best Paper Award
2013 MMM'13 Best Paper Award
2012 ACM Multimedia'12 Grand Challenge Multimodal Award
2011 Hong Kong Scholar
2009 Tsinghua Friendship - Jiang Zhen Scholarship
2005 Excellent Graduate Student of Beijing city
2004 University of Science and Technology, Beijing: Precident Medal
2003 China Undergraduate Mathematical Contest in Modeling (CUMCM): National First Prize  
2001-2005 National Scholarship for 4 successive years  

Professional Experiences
  • Program Chair: ACM CIKM 2019, MMM 2020.
  • Associate Editor: ACM Trans. Intelligent System and Technology (2019 -), IEEE Trans. Big Data (IEEE TBD) (2018 -), IEEE Trans. Knowledge and Data Engineering (IEEE TKDE) (2017-), ACM Trans. Multimedia Computing, Communications and Applications (ACM TOMM) (2016-), Knowledge and Information Systems Journal (2018 -), Neurocomputing(2015-2018), Frontier of Computer Science (Young AE, 2013-2018)
  • Area Chair (SPC): AAAI 2023, NeurIPS 2022, ICML 2022, UAI 2022, KDD 2022, IJCAI 2022, ICML 2021, KDD 2021, IJCAI 2021, WWW 2020, AAAI 2020, AAAI 2019, IJCAI2018, ACM MM2018, ICDM 2018, ACM MM2017, ICDM2016, ACM MM2015, ICME2015, SocInfo2015, ACM MM2014, ICMR2014(special session), ICME2014(special session), ICASSP2013
  • PC Member: IJCAI2015, KDD2015, AAAI2015, WSDM2015, ICIP2015, KDD2014, CIKM2014, ICMR2013, ACM MM2012
  • Workshop Chair, ICDM2015
  • Guest Editor, IEEE Intelligent Systems Magazine, Special issue on Online Behavioral Analysis and Modeling, 2015
  • Co-Chair, Workshop on Connecting Online and Offline Social Network Analysis, ICDM2014
  • Co-Chair, Special Session on Harvesting and Analyzing Live Social Media Data, ACM ICMR 2014
  • Co-Chair, Special Session on Geo-Social Media Mining, Analysis, Recommendation and Retrieval, IEEE ICME 2014
  • Co-Chair, Workshop on Diffusion Networks and Cascade Analytics, ACM WSDM 2014
  • Sponsor Chair, KDD'2012
  • Chair, Special Session on Multimedia in Online Communities, ICME'2012
  • Guest Editor, Information Retrieval, Special Issue on Information Retrieval for Social Media
  • Co-Chair, Workshop on Social and Behavioral Media Access, in conjunction with ACM International Conference on Mutlimedia, 2011.
  • Finance Chair, 7th International Conference on Advanced Data Mining and Applications, 2011
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