Jian Pei

Research Themes

Applications, Artificial Intelligence & Machine Learning, Trustworthy Computing

Bio

Data science, data mining, databases, information retrieval, computational statistics, applied machine learning and AI.

Education

• Ph.D. Simon Fraser University, 2002

Positions

• Arthur S. Pearse Distinguished Professor of Computer Science
• Professor of Computer Science
• Chair of the Department of Computer Science
• Professor of Electrical and Computer Engineering
• Professor of Biostatistics & Bioinformatics

Courses Taught

• ECE 899: Special Readings in Electrical Engineering
• ECE 590: Advanced Topics in Electrical and Computer Engineering
• ECE 583: Data Science
• COMPSCI 590: Advanced Topics in Computer Science
• COMPSCI 526: Data Science
• COMPSCI 394: Research Independent Study
• COMPSCI 393: Research Independent Study
• COMPSCI 391: Independent Study
• CBB 590: Special Topics in Computational Biology
• CBB 526: Data Science
• BRAINSOC 795T: Bass Connections in Brain & Society Research Team
• BRAINSOC 395T: Bass Connections in Brain & Society Research Team

Publications

• Shen K, Wu L, Tang S, Xu F, Long B, Zhuang Y, et al. Ask Questions With Double Hints: Visual Question Generation With Answer-Awareness and Region-Reference. IEEE transactions on pattern analysis and machine intelligence. 2024 Dec;46(12):9648–60.
• Zhu F, Pei J, Zeller M, Zhang B. The Fourth International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD'24). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2024. p. 6757–8.
• Luo X, Pei J. Applications and Computation of the Shapley Value in Databases and Machine Learning. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2024. p. 630–5.
• Zhang J, Xue R, Fan W, Xu X, Li Q, Pei J, et al. Linear-Time Graph Neural Networks for Scalable Recommendations. In: WWW 2024 - Proceedings of the ACM Web Conference. 2024. p. 3533–44.
• Cong Z, Shi B, Li S, Yang J, He Q, Pei J. FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently. IEEE Transactions on Knowledge and Data Engineering. 2024 Apr 1;36(4):1537–51.
• Li H, Cao H, Feng Y, Li X, Pei J. Optimization of Graph Clustering Inspired by Dynamic Belief Systems. IEEE Transactions on Knowledge and Data Engineering. 2024 Jan 1;36(11):6773–85.
• Huang Y, Sun L, Wang H, Wu S, Zhang Q, Li Y, et al. Position: TRUSTLLM: Trustworthiness in Large Language Models. In: Proceedings of Machine Learning Research. 2024. p. 20166–270.
• Si M, Pei J. Counterfactual Explanation of Shapley Value in Data Coalitions. Proceedings of the VLDB Endowment. 2024 Jan 1;17(11):3332–45.
• Zhang Q, Chu L, Zhao Z, Pei J. Finding Antagonistic Communities in Signed Uncertain Graphs. IEEE Transactions on Knowledge and Data Engineering. 2024 Jan 1;
• Sun Q, Zhang J, Liu J, Xiong L, Pei J, Ren K. Shapley Value Approximation Based on Complementary Contribution. IEEE Transactions on Knowledge and Data Engineering. 2024 Jan 1;36(12):9263–81.
• Zhang M, Pei J. Protecting Data Buyer Privacy in Data Markets. IEEE Internet Computing. 2024 Jan 1;28(4):14–20.
• Xing N, Cai S, Chen G, Luo Z, Chin Ooi B, Pei J. Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems. In: Proceedings of the VLDB Endowment. 2024. p. 1020–33.
• Zhou C, Li Q, Li C, Yu J, Liu Y, Wang G, et al. A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT. International Journal of Machine Learning and Cybernetics. 2024 Jan 1;
• Zhuang S, Shou L, Pei J, Gong M, Ren H, Zuccon G, et al. Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval. In: SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region. 2023. p. 212–22.
• Wu N, Gong M, Shou L, Pei J, Jiang D. RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation. In: International Conference on Information and Knowledge Management, Proceedings. 2023. p. 4871–8.
• Jiang H, Yu H, Cheng X, Pei J, Pless R, Yu J. DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization. IEEE Transactions on Knowledge and Data Engineering. 2023 Oct 1;35(10):10831–44.
• Shi H, Tayebi MA, Pei J, Cao J. Cost-Sensitive Learning for Medical Insurance Fraud Detection With Temporal Information. IEEE Transactions on Knowledge and Data Engineering. 2023 Oct 1;35(10):10451–63.
• Wu X, Hu Z, Pei J, Huang H. Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 2648–59.
• Wu L, Pei J, Tang J, Xia Y, Guo X. Deep Learning on Graphs: Methods and Applications (DLG-KDD2023). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 5891–2.
• Wu L, Cui P, Pei J, Zhao L, Guo X. Graph Neural Networks: Foundation, Frontiers and Applications. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 5831–2.
• Liu C, Zhou Z, Pei J, Zhang Y, Shi Y. Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence. IEEE Transactions on Automatic Control. 2023 Aug 1;68(8):4650–65.
• Wu S, Zhao R, Zheng Y, Pei J, Liu B. Identify Event Causality with Knowledge and Analogy. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023. p. 13745–53.
• Xu Z, Shou L, Pei J, Gong M, Su Q, Quan X, et al. A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023. p. 13861–8.
• Xu J, Hong N, Xu Z, Zhao Z, Wu C, Kuang K, et al. Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing. Engineering. 2023 Jun 1;25:66–76.
• Xia L, Huang C, Xu Y, Pei J. Multi-Behavior Sequential Recommendation With Temporal Graph Transformer. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6099–112.
• Li H, Xu W, Qiu C, Pei J. Fast Markov Clustering Algorithm Based on Belief Dynamics. IEEE transactions on cybernetics. 2023 Jun;53(6):3716–25.
• Zhang Z, Niu C, Cui P, Pei J, Zhang B, Zhu W. Permutation-Equivariant and Proximity-Aware Graph Neural Networks With Stochastic Message Passing. IEEE Transactions on Knowledge and Data Engineering. 2023 Jun 1;35(6):6182–93.
• Fionda V, Hartig O, Abdolazimi R, Amer-Yahia S, Chen H, Chen X, et al. Tutorials at The Web Conference 2023. In: Companion Proceedings of the ACM Web Conference 2023. ACM; 2023. p. 648–58.
• Peng J, Zou H, Liu J, Li S, Jiang Y, Pei J, et al. Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping. In: ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. 2023. p. 1220–30.
• Huang R, Wang J, Song S, Lin X, Zhu X, Pei J. Efficiently Cleaning Structured Event Logs: A Graph Repair Approach. ACM Transactions on Database Systems. 2023 Mar 14;48(1).
• Zhang Z, Cui P, Pei J, Wang X, Zhu W. Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. IEEE Transactions on Knowledge and Data Engineering. 2023 Mar 1;35(3):2544–55.
• Zhou C, Li Q, Li C, Yu J, Liu Y, Wang G, et al. A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT. 2023.
• Zou H, Wang H, Xu R, Li B, Pei J, Ye J, et al. Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. In: Proceedings of Machine Learning Research. 2023. p. 350–70.
• Chen N, Shou L, Pei J, Gong M, Cao B, Chang J, et al. Alleviating Over-smoothing for Unsupervised Sentence Representation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023. p. 3552–66.
• Chen N, Shou L, Song T, Gong M, Pei J, Chang J, et al. Structural Contrastive Pretraining for Cross-Lingual Comprehension. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023. p. 2042–57.
• Le TD, Li J, Ness R, Triantafillou S, Shimizu S, Cui P, et al. Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. In: Proceedings of Machine Learning Research. 2023. p. 1–2.
• Xia L, Shao Y, Huang C, Xu Y, Xu H, Pei J. Disentangled Graph Social Recommendation. In: Proceedings - International Conference on Data Engineering. 2023. p. 2332–44.
• Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Applications of Differential Privacy in Social Network Analysis: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2023 Jan 1;35(1):108–27.
• Zhang J, Xia H, Sun Q, Liu J, Xiong L, Pei J, et al. Dynamic Shapley Value Computation. In: Proceedings - International Conference on Data Engineering. 2023. p. 639–52.
• Wang H, Zhu Q, Pei J. Clinical Assessment of Pneumocystosis with MIMIC Data. In: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. 2023. p. 2751–3.
• Pei J, Fernandez RC, Yu X. Data and AI Mo del Markets: Opportunities for Data and Model Sharing, Discovery, and Integration. In: Proceedings of the VLDB Endowment. 2023. p. 3872–3.
• Xue R, Han H, Torkamani MA, Pei J, Liu X. LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation. In: Proceedings of Machine Learning Research. 2023. p. 38926–37.
• Kang J, Zhang S, Li B, He J, Pei J, Zhou D. TrustLOG: The First Workshop on Trustworthy Learning on Graphs. In: International Conference on Information and Knowledge Management, Proceedings. 2022. p. 5169–70.
• Pei J. A Survey on Data Pricing: From Economics to Data Science. IEEE Transactions on Knowledge and Data Engineering. 2022 Oct 1;34(10):4586–608.
• Wu L, Cui P, Pei J, Zhao L, Guo X. Graph Neural Networks: Foundation, Frontiers and Applications. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 4840–1.
• Zhang Y, Gao S, Pei J, Huang H. Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 2515–23.
• Wu L, Pei J, Tang J, Xia Y, Guo X. Deep Learning on Graphs: Methods and Applications (DLG-KDD2022). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022. p. 4906–7.
• Cong Z, Luo X, Pei J, Zhu F, Zhang Y. Data pricing in machine learning pipelines. Knowledge and Information Systems. 2022 Jun 1;64(6):1417–55.
• Zhang Y, Wu L, Shen Q, Pang Y, Wei Z, Xu F, et al. Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation. In: WWW 2022 - Proceedings of the ACM Web Conference 2022. 2022. p. 2153–62.
• Zhang Y, Gao H, Pei J, Huang H. Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction. In: WWW 2022 - Proceedings of the ACM Web Conference 2022. 2022. p. 1352–61.
• Pang Y, Wu L, Shen Q, Zhang Y, Wei Z, Xu F, et al. Heterogeneous global graph neural networks for personalized session-based recommendation. In: WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 2022. p. 775–83.
• Lin W, Shou L, Gong M, Jian P, Wang Z, Byrne B, et al. Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets. In: CEUR Workshop Proceedings. 2022. p. 45–52.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Zuo X, et al. Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022. 2022. p. 9903–18.
• Singh G, Chu L, Wang L, Pei J, Tian Q, Zhang Y. Mining Minority-Class Examples with Uncertainty Estimates. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2022. p. 258–71.
• Fan Z, Fang H, Zhou Z, Pei J, Friedlander MP, Zhang Y. Fair and efficient contribution valuation for vertical federated learning. arXiv preprint arXiv:220102658. 2022;
• Wu L, Cui P, Pei J, Zhao L, Song L. Graph neural networks. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 27–37.
• Zhao L, Wu L, Cui P, Pei J. Representation Learning. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 3–15.
• Cui P, Wu L, Pei J, Zhao L, Wang X. Graph Representation Learning. In: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore; 2022. p. 17–26.
• Chen N, Shou L, Gong M, Pei J. From good to best: Two-stage training for cross-lingual machine reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2022. p. 10501–8.
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization. J Mach Learn Res. 2022;23:36–1.
• Liu N, Wang X, Bo D, Shi C, Pei J. Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum. In: Advances in Neural Information Processing Systems. 2022.
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization. Journal of Machine Learning Research. 2022 Jan 1;23.
• Charette L, Chu L, Chen Y, Pei J, Wang L, Zhang Y. Cosine Model Watermarking Against Ensemble Distillation. arXiv preprint arXiv:220302777. 2022;
• Chen N, Shou L, Gong M, Pei J, Jiang D. Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling. arXiv preprint arXiv:220405210. 2022;
• Li Z, Huang C, Xia L, Xu Y, Pei J. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. arXiv preprint arXiv:220408587. 2022;
• Lin W, Shou L, Gong M, Jian P, Wang Z, Byrne B, et al. Transformer-Empowered Content-Aware Collaborative Filtering. arXiv preprint arXiv:220400849. 2022;
• Shi H, Yang Y, Wang L, Ma D, Beg MF, Pei J, et al. Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction. Journal of Computational and Graphical Statistics. 2022 Jan 1;31(4):1127–40.
• Ren H, Shou L, Pei J, Wu N, Gong M, Jiang D. Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval. In: Findings of the Association for Computational Linguistics: EMNLP 2022. 2022. p. 444–59.
• Bajaj M, Chu L, Romaniello V, Singh G, Pei J, Zhou Z, et al. Revealing Unfair Models by Mining Interpretable Evidence. arXiv preprint arXiv:220705811. 2022;
• Zhuang S, Ren H, Shou L, Pei J, Gong M, Zuccon G, et al. Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. arXiv preprint arXiv:220610128. 2022;
• Li J, Pei J, Huang H. Communication-Efficient Robust Federated Learning with Noisy Labels. arXiv preprint arXiv:220605558. 2022;
• Cong Z, Luo X, Pei J, Zhu F, Zhang Y. Data pricing in machine learning pipelines. Knowledge and Information Systems. 2022;1–39.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Zuo X, et al. Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. arXiv preprint arXiv:220503656. 2022;
• Zhang H, Wu B, Yuan X, Pan S, Tong H, Pei J. Trustworthy Graph Neural Networks: Aspects, Methods and Trends. arXiv preprint arXiv:220507424. 2022;
• Luo X, Pei J, Cong Z, Xu C. On Shapley Value in Data Assemblage Under Independent Utility. In: Proceedings of the VLDB Endowment. 2022. p. 2761–73.
• Wu L, Cui P, Pei J, Zhao L. Graph Neural Networks: Foundations, Frontiers, and Applications. 2022.
• Han J, Pei J, Tong H. Data Mining: Concepts and Techniques, Fourth Edition. In 2022. p. 1–752.
• Zhang Y, Bao R, Pei J, Huang H. Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2022. p. 1317–22.
• Hu X, Chu L, Pei J, Liu W, Bian J. Model complexity of deep learning: a survey. Knowledge and Information Systems. 2021 Oct 1;63(10):2585–619.
• Zhu F, Pei J. The Third International Workshop on Smart Data for Blockchain and Distributed Ledger (SDBD2021): Joint Workshop with SIGKDD 2021 Trust Day. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4185–6.
• Zhang Q, Gu B, Deng C, Gu S, Bo L, Pei J, et al. AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 3917–27.
• Liang S, Gong M, Pei J, Shou L, Zuo W, Zuo X, et al. Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 3231–9.
• Wu L, Tang J, Xia Y, Pei J, Guo X. The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21). In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4167–8.
• Pei J, Zhu F, Cong Z, Luo X, Liu H, Mu X. Data Pricing and Data Asset Governance in the AI Era. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4058–9.
• Shou L, Gong M, Pei J, Geng X, Zhou X, Jiang D. Language Scaling. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM; 2021. p. 4068–9.
• Zhou Z, Chu L, Liu C, Wang L, Pei J, Zhang Y. Towards Fair Federated Learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 4100–1.
• Banitalebi-Dehkordi A, Vedula N, Pei J, Xia F, Wang L, Zhang Y. Auto-Split: A General Framework of Collaborative Edge-Cloud AI. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021. p. 2543–53.
• Liang S, Shou L, Pei J, Gong M, Zuo W, Jiang D. CalibreNet: Calibration Networks for Multilingual Sequence Labeling. In: WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021. p. 842–50.
• Yu W, He X, Pei J, Chen X, Xiong L, Liu J, et al. Visually aware recommendation with aesthetic features. VLDB Journal. 2021 Jul 1;30(4):495–513.
• Liu J, Xiong L, Pei J, Luo J, Zhang H, Yu W. Group-Based Skyline for Pareto Optimal Groups. IEEE Transactions on Knowledge and Data Engineering. 2021 Jul 1;33(7):2914–29.
• Liu J, Xiong L, Zhang Q, Pei J, Luo J. Eclipse: Generalizing kNN and skyline. In: Proceedings - International Conference on Data Engineering. 2021. p. 972–83.
• Wang P, Zheng W, Wang J, Pei J. Automating entity matching model development. In: Proceedings - International Conference on Data Engineering. 2021. p. 1296–307.
• Hu X, Chu L, Pei J, Liu W, Bian J. Model Complexity of Deep Learning: A Survey. 2021.
• Yang Y, Pei J. Influence Analysis in Evolving Networks: A Survey. IEEE Transactions on Knowledge and Data Engineering. 2021 Mar 1;33(3):1045–63.
• Shi H, Ma D, Nie Y, Faisal Beg M, Pei J, Cao J, et al. Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis. Journal of medical imaging (Bellingham, Wash). 2021 Mar;8(2):024502.
• Liu J, Yang J, Xiong L, Pei J, Luo J, Guo Y, et al. Skyline diagram: Efficient space partitioning for skyline queries. IEEE Transactions on Knowledge and Data Engineering. 2021 Jan 1;33(1):271–86.
• Huang H, Geng X, Jian P, Long G, Jiang D. Reasoning over entity-action-location graph for procedural text understanding. In: ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. 2021. p. 5100–9.
• Fan Z, Fang H, Zhou Z, Pei J, Friedlander MP, Liu C, et al. Improving fairness for data valuation in federated learning. arXiv preprint arXiv:210909046. 2021;
• Yuan F, Shou L, Pei J, Lin W, Gong M, Fu Y, et al. Reinforced Multi-Teacher Selection for Knowledge Distillation. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 14284–91.
• Liu C, Zhou Z, Shi Y, Pei J, Chu L, Zhang Y. Achieving Model Fairness in Vertical Federated Learning. arXiv preprint arXiv:210908344. 2021;
• Chu L, Wang L, Dong Y, Pei J, Zhou Z, Zhang Y. Fedfair: Training fair models in cross-silo federated learning. arXiv preprint arXiv:210905662. 2021;
• Cho-Ho Lam P, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding Representative Interpretations on Convolutional Neural Networks. arXiv e-prints. 2021;arXiv-2108.
• Lam PC-H, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding representative interpretations on convolutional neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. p. 1345–54.
• Shou L, Gong M, Pei J, Geng X, Zhou X, Jiang D. Language Scaling: Applications, Challenges and Approaches. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. p. 4068–9.
• Xu C, Zhang C, Xu J, Pei J. SlimChain: scaling blockchain transactions through off-chain storage and parallel processing. Proceedings of the VLDB Endowment. 2021;14:2314–26.
• Lam PCH, Chu L, Torgonskiy M, Pei J, Zhang Y, Wang L. Finding Representative Interpretations on Convolutional Neural Networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2021. p. 1325–34.
• Xu C, Zhang C, Xu J, Pei J. Slimchain: Scaling blockchain transactions through off-chain storage and parallel processing. In: Proceedings of the VLDB Endowment. 2021. p. 2314–26.
• Cong Z, Chu L, Yang Y, Pei J. Comprehensible counterfactual explanation on Kolmogorov-Smirnov test. In: Proceedings of the VLDB Endowment. 2021. p. 1583–96.
• Liu J, Lou J, Xiong L, Pei J, Sun J. Dealer: An end-to-end model marketplace with differential privacy. Proceedings of the VLDB Endowment. 2021 Jan 1;14(6):957–69.
• Bajaj M, Chu L, Xue ZY, Pei J, Wang L, Lam PCH, et al. Robust Counterfactual Explanations on Graph Neural Networks. In: Advances in Neural Information Processing Systems. 2021. p. 5644–55.
• Xia L, Huang C, Xu Y, Dai P, Zhang X, Yang H, et al. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 4486–93.
• Wu L, Chen Y, Shen K, Guo X, Gao H, Li S, et al. Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:210606090. 2021;
• Hu X, Chu L, Pei J, Bian J, Liu W. Deep Learning Model Complexity: Concepts and Approaches. 2021;
• Wang H, Xu C, Zhang C, Xu J, Peng Z, Pei J. vChain+: Optimizing Verifiable Blockchain Boolean Range Queries (Technical Report). 2021;
• Zhou Y, Geng X, Shen T, Pei J, Zhang W, Jiang D. Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021. p. 4586–96.
• Lin Q, Zhang J, Liu J, Ren K, Lou J, Xiong L, et al. Demonstration of dealer: An end-to-end model marketplace with differential privacy. In: Proceedings of the VLDB Endowment. 2021. p. 2747–50.
• Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, et al. Personalized Cross-Silo Federated Learning on Non-IID Data. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021. p. 7865–73.
• Guo Y, Shou L, Pei J, Gong M, Xu M, Wu Z, et al. Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding. In: EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings. 2021. p. 3226–37.
• Cong Z, Chu L, Yang Y, Pei J. Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test. 2020.
• Liu J, Shou L, Pei J, Gong M, Yang M, Jiang D. Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation. 2020.
• Zhang X, Shou L, Pei J, Gong M, Wen L, Jiang D. A Graph Representation of Semi-structured Data for Web Question Answering. 2020.
• Jiang H, Pei J, Yu D, Yu J, Gong B, Cheng X. Applications of Differential Privacy in Social Network Analysis: A Survey. 2020.
• Pei J. A Survey on Data Pricing: from Economics to Data Science. 2020.
• Wang X, Zhu M, Bo D, Cui P, Shi C, Pei J. AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 1243–53.
• Pei J. Data Pricing - From Economics to Data Science. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 3553–4.
• Wang F, Cui P, Pei J, Song Y, Zang C. Recent Advances on Graph Analytics and Its Applications in Healthcare. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 3545–6.
• Shou L, Bo S, Cheng F, Gong M, Pei J, Jiang D. Mining Implicit Relevance Feedback from User Behavior for Web Question Answering. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 2931–41.
• Hu X, Liu W, Bian J, Pei J. Measuring Model Complexity of Neural Networks with Curve Activation Functions. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020. p. 1521–31.
• Choi DW, Pei J, Lin X. On spatial keyword covering. Knowledge and Information Systems. 2020 Jul 1;62(7):2577–612.
• Yu W, Liu J, Pei J, Xiong L, Chen X, Qin Z. Efficient Contour Computation of Group-Based Skyline. IEEE Transactions on Knowledge and Data Engineering. 2020 Jul 1;32(7):1317–32.
• Shou L, Bo S, Cheng F, Gong M, Pei J, Jiang D. Mining Implicit Relevance Feedback from User Behavior for Web Question Answering. 2020.
• Ning G, Pei J, Huang H. LightTrack: A generic framework for online top-down human pose tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2020. p. 4456–65.
• Yang Y, Mao X, Pei J, He X. Continuous influence maximization. ACM Transactions on Knowledge Discovery from Data. 2020 May 8;14(3).
• Amer-Yahia S, Pei J. VLDB SI 2018 editorial. VLDB Journal. 2020 May 1;29(2–3):593–4.
• Cong Z, Chu L, Wang L, Hu X, Pei J. Exact and consistent interpretation of piecewise linear models hidden behind APIs: A closed form solution. In: Proceedings - International Conference on Data Engineering. 2020. p. 613–24.
• Liu J, Shou L, Pei J, Gong M, Yang M, Jiang D. Cross-lingual machine reading comprehension with language branch knowledge distillation. arXiv preprint arXiv:201014271. 2020;
• Zhang X, Shou L, Pei J, Gong M, Wen L, Jiang D. A graph representation of semi-structured data for web question answering. arXiv preprint arXiv:201006801. 2020;
• Huang F, Gao S, Pei J, Huang H. Momentum-based policy gradient methods. In: International conference on machine learning. 2020. p. 4422–33.
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• Pei J, Tung AKH, Han J. Fault-Tolerant Frequent Pattern Mining: Problems and Challenges. DMKD. 2001;1:270–270.
• Pinto H, Han J, Pei J, Wang K, Chen Q, Dayal U. Multi-dimensional sequential pattern mining. In: International Conference on Information and Knowledge Management, Proceedings. 2001. p. 81–8.
• Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC. FreeSpan: Frequent pattern-projected sequential pattern mining. In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000. p. 355–9.
• Pei J, Mao R, Hu K, Zhu H. Tow ards Data Mining Benchmarking: A Test Bed for Performance Study of Frequent Pattern Mining. SIGMOD Record. 2000 Jan 1;29(2):592.
• Han J. Frequent pattern-projected sequential pattern mining. Proc of the ACM SIGKDD, 2000. 2000;
• Han J, Pei J, Yin Y. Mining Frequent Patterns without Candidate Generation. In: SIGMOD 2000 - Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000. p. 1–12.
• Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. SIGMOD Record (ACM Special Interest Group on Management of Data). 2000 Jan 1;29(2):1–12.
• Pei J, Han J. Can we push more constraints into frequent pattern mining? In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000. p. 350–4.
• Pei J, Han J, Mortazavi-Asl B, Zhu H. Mining access patterns efficiently from web logs. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2000. p. 396–407.
• Pei J, Han J, Mortazavi-Asl B. Zhu. H. 2000. Mining access patterns efficiently from Web Logs. In: Proceedings of. 2000.
• Han J, Pei J. Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD explorations newsletter. 2000;2:14–20.
• Pei J, Han J, Mao R, others. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery. 2000. p. 21–30.
• Pei J, Chai W, Zhao C, Tang S, Yang D. Algebra for online analytical processing data cube. Acta Metallurgica Sinica (English Letters). 1999 Oct 1;12(5):561–9.
• Han J, Pei J, Yin Y. Mining partial periodicity using frequent pattern trees. CS Tech. Rep. 99-10, Simon Fraser University; 1999.
• Tang C, Zhang A, Pei J. Genes. 1997;
• Pei Z, Chen J, Zhu M, Pei J, Liu Z, Tan Y. Plasma levels of endothelin-1, nitric oxide, malondialdehyde and superoxide dismutase in rats exposed to infrasound. Chinese Journal of Pathophysiology. 1986;
• Chomicki J, Pei J, Srihari RK, Yang G, Zhang A. Database Research at University at Buffalo.
• Xu Y, Wang K, Fu AW-C, She R, Pei J. Privacy-Preserving Classification for Data Streams.
• Chu HWF, Fan W, Philip SY, Pei J. A Fast Algorithm for Subspace Clustering by Pattern Similarity. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM�04). p. 20–00.
• Bonchi F, Cao L, Chen M-S, Cook DJ, Gaussier E, Goethals B, et al. Steering Committee DSAA 2020.
• Cook D, Pei J, Wang W, Zaïane O, Wu X. 11th IEEE International Conference on Data Mining.
• Liao Z, Jiang D, Pei J, Kang D, Sun X, Choi H-J, et al. Constructing a Multidimensional Topic-Concept Cube for OLAP on Large-Scale Search Logs.
• Pei J, Han J, Mao R. CLOSET: An E cient Algorithm for Mining Frequent Closed Itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. p. 21–30.
• Huang F, Gao S, Pei J, Huang H. Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization Download PDF.
• Pei J, Han J, Mortazavi-asl B, Zhu H. Mining Access Patterns E ciently from Web Logs? In: Proc of PAKDD’OO.
• Pei J, Ariwala SR, Jiang D. Online mining changes of clusters in data streams. Submitted for publication.
• Pei J, Xiong H. Mining Co-location Patterns with Rare Events from Spatial Data Sets* Yan Huang (huangyan@ unt. edu) Dept. of Computer Science and Engineering, University of North Texas, USA.
• Yang Y, Mao X, Pei J, He X. Continuous Influence Maximization: Algorithmic Framework and Implementation under Triggering Models.
• Kung S-Y, Zomaya A, Qiu M, Yan Z, Wu Y, Chen J, et al. Committee Members BigDataSE 2018.
• Chen J, Yang LT, Kotagiri R, Pei J, Sun X-H, Sahni S, et al. DSS 2017.
• Huang F, Gao S, Pei J, Huang H. Momentum-Based Policy Gradient Methods Download PDF.
• Pei J. SPOTLIGHT ON TRANSACTIONS.
• Tao Y, Li J, Ding L, Lin X, Pei J. On Representing Skylines by Distance.
• Bonchi F, Cao L, Chen M-S, Cook DJ, Gaussier E, Goethals B, et al. DSAA 2019.
• Han J, Kamber M, Pei J. 4 Major Tasks in Data Preprocessing.
• Pei J, Han J, Mortazavi-asl B, Pinto H, Chen Q, Dayal U. chun Hsu, M.(2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE�01: Proceedings of the 2001 International Conference on Data Engineering. p. 215–215.
• Pei J. The Pattern-based Approaches to Mining Microarray Data.
• Jiang B, Pei J, Lin X, Cheung DW, Han J. Mining Preferences from Superior and Inferior Examples TR 2008-09.
• Xing Z, Pei J. Running Title: Exploring Disease Association from the NHANES Data.
• Discovery P-ACOK, Mining D, Pei J. Advances in knowledge discovery and data mining: 17th Pacific-Asia conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013: proceedings. Springer;
• Pei J. Towards Trustworthy Data Science: Interpretability, Fairness and Marketplaces.
• Jamil H, Pei J, Cuzzocrea A. Session details: Artificial intelligence & agents, information systems, and software development: Data mining track.
• Crago S, Dustdar S, Foster I, Fox G, Guo M, Herrera F, et al. BigDataSE 2021.
• Wang H, Xu C, Zhang C, Xu J, Peng Z, Pei J. vChain+: Optimizing Verifiable Blockchain Boolean Range Queries.
• Wong RC-W, Fu AW-C, Pei J, Wang K, Wan SC-W, Lo CS. Multidimensional k-Anonymization by Linear Clustering Using Space-Filling Curves Technical Report TR 2006-27.
• Pei J, Wang K. Online Skyline Analysis with Dynamic Preferences on Nominal Attributes.
• Fox GC, Sun X-H, Pei J, Zhang X, Li X, Chen J, et al. BigDataMR2012 Organizing and Program Committees.
• Steinbach M, Kumar V, Han J, Kamber M, Pei J. MSCIT 5210: Knowledge Discovery and Data Mining.
• Chakrabarti D, Chang E, Chapelle O, Clifton C, Davison B, Ding C, et al. SIGKDD-2010 Program Committees.
• Zhou Z-H, Wang W, Kumar R, Toivonen H, Pei J, Huang JZ, et al. ICDMW 2014 14th IEEE International Conference on Data Mining Workshops Shenzhen, China/14 December 2014.
• Bailey J, Berendt B, Cao L, Chawla AN, Chen X, Diesner J, et al. Ulrik Brandes, University of Konstanz, Germany Piotr Br6dka, Wroc1aw University of Technology, Poland Yi Cai, South China University of Technology, China.
• Cui J, Pei J, Gama JA. The International Conference on Advanced Data Mining And Application ADMA2009.
• Kumar R, Toivonen H, Pei J, Huang JZ, Wu X. ICDM 2014 14th IEEE International Conference on Data Mining Shenzhen, China/14-17 December 2014.
• Pei J, Upadhyaya SJ, Farooq F, Govindaraju V. Data Mining for Intrusion Detection.
• Cercone N, Cheng X, Hu X, Nambiar R, Pei J, Raghavan V, et al. Big Data Steering Committee.
• Pei J, Zhou B, Tang Z, Huang D. Data Mining Techniques for Web Spam Detection. Simon Fras University Microsoft Ad Center.
• Pei J, Hua M, Tao Y, Lin X. Mining Uncertain and Probabilistic Data.
• Wong RC-W, Fu AW-C, Wang K, Pei J. Minimality Attack in Privacy Preserving Data Publishing Technical Report TR 2006-28.
• Bailey BANSJ, Pei J. Document Clustering of Scientific Texts Using Citation Contexts.
• Chen Y, Dong G, Pei J, Wah BW, Wang J. Stream C ube: A n A rchitecture for.

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