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Yu Dai, Junchen Shen, Zijie Zhai, Danlin Liu, Jinyang Chen, Yu Sun, Ping Li, Jie Zhang, Kai Zhang. High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion. Proceedings of the 41st International Conference on Machine Learning, ICML 2024.
Kai Zhang, Junchen Shen, Gaoqi He, Yu Sun, Hongyuan Zha, Haibin Ling, Hongli li, Jie Zhang. A Transformative Topological Representation for Link Modelling, Prediction and Cross-Domain Network Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI 2024.
Pan Li, Ping Li, Kai Zhang. Dual-Channel Span for Aspect Sentiment Triplet Extraction. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023.
Juyong Jiang, Bingqing Wu, Ling Chen, Kai Zhang, Sunghun Kim, Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting, CIKM 2023.
Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim and Jaeboum KimAdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation, CIKM 2023。
Yue Qu, Chuanren Liu, Kai Zhang, Keli Xiao, Bo Jin, Hui Xiong, Diagnostic Sparse Connectivity Networks With Regularization Template, IEEE Transactions on Knowledge and Data Engineering 2023, Jan. vol. 35, pp. 307-320.
Xinran Wu, Lena Palaniyappan, Gechang Yu, Kai Zhang, Jakob Seidlitz, Zhaowen Liu, Xiangzhen Kong, Gunter Schumann, Jianfeng Feng, Barbara J. Sahakian, Trevor W. Robbins, Edward Bullmore & Jie Zhang. Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Nature Molecular Psychiatry 2022.
Jie Wang, Zihao Shen, Yichen Liao, Zhen Yuan, Shiliang Li, Gaoqi He, Man Lan, Xuhong Qian, Kai Zhang, Honglin Li. Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space. Brief in Bioinformatics,23(6):2022
Xinran Wu, Xiangzhen Kong, Deniz Vatansever, Zhaowen Liu, Kai Zhang, Barbara J. Sahakian, Trevor W. Robbins, Jianfeng Feng, Paul Thompson, Jie Zhang. Dynamic changes in brain lateralization correlate with human cognitive performance, PLOS Biology, March 17,2022.
Yaokang Zhu, Kai Zhang, Jun Wang, Jie Zhang, Hongyuan Zha, Haibin Ling, Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph ClassificationProceedings of the AAAI Conference on Artificial Intelligence AAAI 2022.
Zhu, Yaokang, Jun Wang, Jie Zhang, Kai Zhang. Node Embedding and Classification with Adaptive Structural Fingerprint. Neurocomputing 502: 196-208 (2022).
Tianyi Gu; Kaiwen Huang; Jie Zhang; Kai Zhang; Ping Li; Fast Convolutional Factorization Machine with Enhanced Robustness, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
GMOT-40: A Benchmark for Generic Multiple Object Tracking.Hexin Bai, Wensheng Cheng, Peng Chu, Juehuan Liu, Kai Zhang, Haibin Ling; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 6719-6728.
Conor Tillinghast, Shikai Fang, Kai Zhang, and Shandian Zhe. Probabilistic Neural-Kernel Tensor Decom-position. International Conference on Data Mining (ICDM 2020).
Shikai Fang, Shandian Zhe, Kuang-chih Lee, Online Bayesian Sparse Learning with Spike and Slab Pri-ors”. International Conference on Data Mining.Juyong Jiang, Jie Zhang, Kai Zhang (ICDM 2020).
Cascaded Semantic and Positional Self-Attention Network for Doc-ument Classification. Findings of the Association for Computational Linguistics: EMNLP 2020.
Kai Zhang, Yaokang Zhu, Jie Zhang, Jun Wang. Adaptive Structural Fingerprints for Graph AttentionNetworks.8th International Conference on Learning Representations (ICLR 2020).
Kai Zhang, Jun Liu, Jie Zhang, Jun Wang. Greedy Orthogonal Pivoting for Non-negative Matrix Factor-ization.36th International Conference on Machine Learning (ICML 2019).
Yanjun Li,Kai Zhang, Jun Wang, Sanjiv Kumar. Learning Adaptive Random Features.33th AAAIConference on Artificial Intelligence (AAAI 2019).
Wenchao Yu, Wei Cheng, Charu C. Aggarwal,Kai Zhang, Haifeng Chen, and Wei Wang. NetWalk:A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks.ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining (KDD 2018).
Hancheng Ge,Kai Zhang, Xia Hu, James Caverlee. A distributed Algorithm for Tensor Completion on Spark,Proceedings of the International Conference on Data Engineering (ICDE 2018).
Liang Lan, Zhuang Wang, Wei Cheng,Kai Zhang*. Scaling up Kernel SVM on Limited Resources: ALow-rank Linearization Approach,IEEE Transactions on Neural Networks and Learning Systems (TNNLS2018).
Xiaohan Zhao, Bo Zong, Ziyu Guan,Kai Zhang, Wei Zhao. Substructure Assembling Network for GraphClassification.Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI 2018).
Y Lin, Z Chen, C Cao, L Tang, K Zhang, W Cheng, Z Li. Collaborative Alert Ranking for Anomaly Detection. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018).
Zhang J, Xie X, Rolls ET, Sun J, Zhang K, Jiao Z, Chen Q, Zhang J, Qiu J, Feng J, Neural and GeneticDeterminants of Creativity, NeuroImage, 2018 July 1;(174):164-176.
Jie Zhang, Zhigen Zhao,Kai Zhang, and Zhi Wei, A Feature Sampling Strategy for Analysis of HighDimensional Genomic Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB2018).
Ruihua Cheng, Zhi Wei,Kai Zhang. Network Inference from Contrastive Groups Using DiscriminativeStructural Regularization,SIAM Conference of Data Mining (SDM 2018).
Ping Li, Kaiqi Chen, Yi Ge,Kai Zhangand Michael Small Mining higher-order network structures viamotif-vertex-interactions,Europhysics Letters (EPL 2018).
Kai Zhang, Chuanren Liu, Jie Zhang, Eric Xing, Hui Xiong, Jieping Ye .Randomization or Condensation?:Linear Cost Matrix Sketching Via Cascaded Compression Sampling.Proceedings of the 23rd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining (KDD 2017).
Kai Zhang,Jingchao Ni,Wei Cheng,Kai Zhang, Dongjin Song , Tan Yan , Haifeng Chen and Xiang Zhang, RankingCausal Anomalies by Modeling Local Propagations on Networked Systems,IEEE International Conferenceon Data Mining (ICDM 2017).
B. Dong, C. Zheng, H.Wang, L. Tang,K. Zhang, Y. Lin, Z. Li, and H. Chen, Efficient Discovery of Abnor-mal Event Sequences in Enterprise Security Systems,Proceedings of the ACM on Conference on Informationand Knowledge Management (CIKM 2017).
Jie Zhang, Wei Cheng, Zhaowen Liu,Kai Zhang*, Xu Lei, Ye Yao, Benjamin Becker, Yicen Liu, KeithM. Kendrick, Guangming Lu, Jianfeng Feng. Neural, electrophsiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.Brain, 139(8): 2307-2321, 2016.Editor’s choice and featured on the cover of the journal, receiving commentary“TheFlexible Brain”from 2014 MacArther fellow D. S. Bassett.
Shandian Zhe,Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani.Distributed Flexible Nonlinear Tensor Factorization.Proceedings of Neural Information Processing Systems29 (NIPS 2016), Barcelona, Spain.
Kai Zhang, Shandian Zhe, Chaoran Cheng, Zhi Wei, Zhengzhang Chen, Haifeng Chan, Guofei Jiang,Alan Qi, Jieping Ye. Annealed Sparsity via Adaptive and Dynamic Shrinking.The 22th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining (KDD 2016).
Wei Cheng,Kai Zhang, Haifeng Chen, Guofei Jiang, Wei Wang. Ranking Causal Anomalies via Temporaland Dynamical Analysis on Vanishing Correlations.The 22th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining(KDD 2016 Best Paper Award Runner-Up Award).
Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen,Kai Zhang. Entity Embedding-based AnomalyDetection for Heterogeneous Categorical Events.International Joint Conference on Artificial Intelligence 2016 (IJCAI 2016) New York City, NY.
Chuanren Liu,Kai Zhang, Hui Xiong, Qiang Yang, Guofei Jiang. Temporal Skeletonization on Sequen-tial Data: Patterns, Categorization, and Visualization.IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 28, 211–223, 2016.
Liang Lan, Kai Zhang, Hancheng Ge,Wei Cheng,Jun Liu,Andreas Rauber, Xiao-Li Li,Jun Wang, Hongyuan Zha. Low-rank Decomposition Meets Kernel Learning: A Generalized Nystr ̈om Method.Artificial Intelligence (AI 2016).
Qiaojun Wang, Kai Zhang, Zhengzhang Chen, Dequan Wang, Guofei Jiang, Ivan Marsic. DesigningLabel-Aware Base Kernels for Semi-supervised Learning. Neural Computing, Vol. 171, No. 1, 1335 – 1343,2016.
Liudmila Ulanova, Tan Yan, Haifeng Chen, Geoff Jiang, Eamonn Keogh,Kai Zhang. Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems.The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), Sydney, Australia, 2015.
Kai Zhang, Liang Lan, James T. Kwok, Vucetic Slobodan, and Bahram Parvin. Scaling up Graph-basedSemi-supervised Learning Via Prototype Vector Machines.IEEE Transactions on Neural Networks & Learning Systems (TNNLS), Vol. 26, No. 3, 444–457, 2015.
Kai Zhang, Qiaojun Wang, Zhengzhang Chen, Guofei Jiang, Jie Zhang. From Categorical to Numerical:Multiple Transitive Distance Learning and Embedding.SIAM Conference on Data Mining(SDM 2015),Vancouver, Canada.
Kai Zhang,Chuanren Liu, Kai Zhang, Hui Xiong, Qiang Yang, Guofei Jiang. Temporal Skeletonization on SequentialData: Patterns, Categorization, and Visualization.The 20th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (KDD 2014), New york, USA.
Kai Zhang, Qiaojun Wang, Liang Lan, Yu Sun, Ivan Marsic. Sparse Semi-supervised Learning on Low-rank Kernel,Neural Computing, Vol. 129, No. 10, 265 – 272, 2013.
Kai Zhang, Vincent W. Zheng, Qiaojun Wang, James T. Kwok, Qiang Yang, Ivan Marsic. Covariate Shift in Hilbert Space: A Solution via Surrogate Kernels. In the30th International Conference on Machine Learning (ICML 2013), Atlanta, Gorgeia.
Kai Zhang, Liang Lan, Jun Liu, Andreas Rauber, Fabian Moerchen. Inductive Kernel Low-rank Decom-position with Priors: A Generalized Nystr ̈om Method. In the29th International Conference on Machine Learning (ICML 2012), Edinburgh, UK.
Kai Zhang, Liang Lan, Zhuang Wang, Fabian Moerchen. Scaling Up Kernel SVM on Limited Resources: A Low-rank Linearization Approach.International Conference on Artificial Intelligence and Statistics (AI&STAT 2012), La Palma, Canary Islands.
Kai Zhang, James T. Kwok. Clustered Nystr ̈om Method for Large Scale Manifold Learning and Dimen-sion Reduction,IEEE Transactions on Neural Networks (TNN), 21 (10): 1576-1587, 2010.
Kai Zhang, James T. Kwok. Simplifying Mixture Models through Function Approximation,IEEE Trans-actions on Neural Networks, 21(4): 644 - 658, 2010.
Kai Zhang, James T. Kwok. Density-Weighted Nystr ̈om Method for Computing Large Kernel Eigen-Systems,Neural Computation, 21(1): 121-146, 2009.
Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical,IEEE Transac-tions on Neural Networks (TNN), 20(4): 583-596, 2009.
Kai Zhang, James T. Kwok, Bahram Parvin. Prototype Vector Machine for Large Scale Semi-supervisedLearning. In the26th International Conference on Machine Learning (ICML 2009), Montreal, 2009.
Kai Zhang, Ivor W. Tsang, James T. Kwok. Improved Nystr ̈om Low Rank Approximation and ErrorAnalysis. In the 25th International Conference on Machine Learning (ICML 2008), Helsinki, 2008.
Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical. In the 24th International Conference on Machine Learning (ICML 2007), Oregen, USA, 2007.
Kai Zhang, James T. Kwok. Simplifying Mixture Models Through Function Approximation. Neural Information Processing Systems 19 (NIPS 2006), Vancouver, Canada, 2006.
Kai Zhang, James T. Kwok. Block-Quantized Kernel Matrix for Fast Spectral Embedding. In the23rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, USA, 2006.
Kai Zhang, James T. Kwok, M. Tang. Accelerated Convergence Using Dynamic Mean Shift. In the 9th European Conference on Computer Vision (ECCV 2006), Graz, Austria.
Ivor W. Tsang, James T. Kwok, Brian Mak,Kai Zhang, Jeffrey J. Pan. Fast Speaker Adaptation via Maximum Penalized Likelihood Kernel Regression. In the International Conference on Acoustics, Speech, andSignal Processing (ICASSP 2006). (Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum)
Kai Zhang, M. Tang, J.T. Kwok. Applying Neighborhood Consistency for Fast Clustering and KernelDensity Estimation.International Conference on Computer Vision and Pattern Recognition (CVPR 2005).
Zhaowen Liu, Jie Zhang, Kai Zhang, Junying Zhang, Xiaojing Li, Wei Cheng,..., Jianfeng Feng, Tao Li.Distinguishable brain networks relate disease susceptibility to symptom expression in schizophrenia.Human Brain Mapping (HBM 2018) DOI: 10.1002/hbm.24190.
Yan Chen, Ping Li,Kai Zhang, and Jie Zhang. Finding communities by their centers.Scientific Reports,6:24017, Nature Publishing Group 2016.
Ping Li, Xian Sun, Kai Zhang, Jie Zhang, Jurgen Kurths. The role of structural holes in containing spreading processes.Physical Review E, 93, 032312, 2016.
Haifeng Chen, Mizoguchi Takehiko, Tan Yan, Kai Zhang, and Geoff Jiang A Quality Control Enginefor Complex Physical Systems.The 45th IEEE/IFIP International Conference on Dependable Systems and Networks(DSN 2015), Rio de Janeiro, Brazil.
Yan Chen, Lixue Chen, Xian Sun,Kai Zhang, Jie Zhang, Ping Li. Coevolutionary dynamics of opinionpropagation and social balance: The key role of small-worldness.The European Physical Journal B, March2014, 87:62.
Ping Li, Xian Sun,Kai Zhang, Jie Zhang. Degree-based attacks are not optimal for desynchronization ingeneral networks,Physical Review E, 88, 022817, 2013.
P. Li,K. Zhang, X. Xu, J. Zhang, M. Small. Re-examination of explosive synchronization in scale-freenetworks: the effect of disassortativity.Physical Review E, 87, 042803, 2013.
J. Zhang, X. Xu, P. Li,K. Zhang, and M. Small. Node Importance for Dynamical Process on Networks: AMultiscale Characterization,Chaos, 21, 016107, 2011.
J. Zhang, K. Zhang, X. Xu, C.K. Tse and M. Small, Seeding the Kernels in Graphs: towards Multi-Resolution Community Analysis,New Journal of Physics, 11, 113003, 2009.
J. Zhang, J. Sun, X. Luo,K. Zhang, T. Nakamura and M. Small, Characterizing Topology of Pseudoperi-odic Time Series via Complex Network Approach,Physica D, 237(22): 2856-2865, November, 2008.
Kai Zhang, Ju Han, Torsten Groesser, Gerald Fontenay, Bahram Parvin, Inference of Causal Networksfrom Time-Varying Trascriptome Data Via Sparse Coding,Plos ONE, Volume 7, e42306, 2012.
S. Nath, V. Spencer, J. Han, H. Chang,K. Zhang, G. Fontenay, J. Hyman, C. Anderson, M. Hamilton, Y.Chang, and B. Parvin. Identification of Fluorescent Compounds with Non-specific Binding Property via High Throughput Live Cell Microscopy,Plos ONE, Volume 7, Issue 1, e28802, 2012.
Q. Wang,K. Zhang, I. Maarsic and J. K-J Li, Patient Friendly Detection of Early Peripheral Arterial Dis-ease (PAD) by Budgeted Sensor Selection,6th International Conference on Pervasive Computing Technologiesfor Healthcare(PH 2012), San Diego, California, 2012.
Ju Han, Hang Chang, Leandro Loss, Kai Zhang, Fredrick L. Baehner, Joe Gray, Paul Spellman, BahramParvin. Comparison of Sparse Coding and Kernel Methods for Histopathological Classification of Gliobas-toma Multiforme. In theIEEE International Symposium on Biomedical Imaging(ISBI 2011), Chicago, Illinois,U.S. 2011.
Jie Zhang, Kai Zhang, Jianfeng Feng, and Michael Small. Rhythmic Dynamics and Synchronization viaDimensionality Reduction: Application to Human Gait,Plos Computational Biology6(12): e1001033, 2010 (highlighted as featured research).
Kai Zhang, Joe W. Gray, and Bahram Parvin, Sparse MultiTask Regression for Identifying Common Mechanism of Response to Therapeutic Targets,18th International Conference on Intelligent Systems forMolecular Biology (ISMB) 2010,Bioinformatics, 26:97-105, 2010
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