Publication

         ML            BioMed           NLP             AI+X

2024

• [IJCV] Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Bin Gu, Huan Xiong, Xinping Yi, Inter-feature Relationship Certifies Robust Generalization of Adversarial Training, International Journal of Computer Vision (IJCV), 2024

• [CVPR] Zhaorui Tan, Xi Yang, Kaizhu Huang, Rethinking Multi-domain Generalization with A General Learning Objective, The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) , 2024.

• [AAAI] Zhaorui Tan, Xi Yang, Kaizhu Huang, Semantic-aware Data Augmentation for Text-to-image Synthesis,  Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.

• [AAAI] Zixian Su, Jingwei Guo, Kai Yao, Xi Yang, Qiu-Feng Wang, Kaizhu Huang, Unraveling Batch Normalization for Realistic Test-Time Adaptation, Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024 (Oral).

• [AAAI] Zihao Zhou, Qiufeng Wang, Mingyu Jin, Jie Yao, Jianan Ye, Wei Liu, Wei Wang, Xiaowei Huang, Kaizhu Huang, MathAttack: Attacking Large Language Models towards Math Solving Ability, Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.

• [ICRA] Wenhui Wei, Jiantao Li, Kaizhu Huang, Jiadong Li, Xin Liu, Yangfan Zhou,  Lite-SVO: Towards A Lightweight Self-Supervised Semantic Visual Odometry Exploiting Multi-Feature Sharing Architecture, 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024.

• [TOIS] Jiezhu Cheng, Kaizhu Huang, Zibin Zheng, Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training, ACM Transactions on Information System (TOIS), 2024

• [TCYB] Penglei Gao,  Xi Yang,  Rui Zhang, Ping Guo, Kaizhu Huang, EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables, IEEE Transactions on Cybernetics (TCYB), 2024.

• [TOMM] Penglei Gao,  Xi Yang,  Rui Zhang, Kaizhu Huang, Continuous Image Outpainting with Neural ODE, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2024. 

• [NN]  Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, X Yi, B Gu, H Xiong, Perturbation diversity certificates robust generalization, Neural Networks (NN), 106117, 2024.

• [PR] Jianan Ye, Yijie Hu, Xi Yang, Qiu-Feng Wang, Kaizhu Huang, SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection, Pattern Recognition (PR), 2024 

2023

• [ICCV] Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, and Kaizhu Huang, Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization, International Conference on Computer Vision (ICCV), 2023. 

• [ICCV] Zhiqiang Gao, Kaizhu Huang, Rui Zhang, Dawei Liu, and Jieming Ma, Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation, International Conference on Computer Vision (ICCV), 2023.

• [WWW] Jingwei Guo, Kaizhu Huang, Xinping Yi, and Rui Zhang, Graph Neural Networks with Diverse Spectral Filtering, ACM Web Conference (WWW), 2023.

• [ICDM] Yiming Lin, Xiaobo Jin, Qiufeng Wang, and Kaizhu Huang, Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching Network , In IEEE International Conference on Data Mining (ICDM), 2023.

• [MM] Maizhen Ning, Qiufeng Wang, Kaizhu Huang, and Xiaowei Huang, A Symbolic Character-Aware Model for Solving Geometry Problems, ACM Multimedia (MM), 2023.

• [CIKM] Kai Yao, Zixian Su, Xi Yang, Jie Sun, and Kaizhu Huang, Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation, In ACM Conference on Information and Knowledge Management (CIKM), 2023.

• [ACL] Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei huang, and Kaizhu Huang, Learning by Analogy: Diverse Questions Generation in Math Word Problem，Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2023.

• [AAAI] Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang, Jie Sun, and Kaizhu Huang, Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.

• [TIP] Zihan Ye, Guanyu Yang, Xiao-Bo Jin, Youfa Liu, Kaizhu Huang, Rebalanced Zero-shot Learning, IEEE Trans. Image Processing (TIP), 2023.

• [TKDD] Jiezhu Chen, Kaizhu Huang, Zibing Zheng, Fitting Imbalanced Uncertainties in Multi-Output Time Series Forecasting，ACM Transactions on Knowledge Discovery from Data (TKDD), 2023.

• [TNNLS] Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang, Learning Disentangled Graph Convolutional Networks Locally and Globally, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

• [TAI] Wei Li, C. Chen, Kaizhu Huang, Absorb and Repel: Pseudo-Label Refinement for Intra-Camera Supervised Person Re-identification, IEEE Transactions on Artificial Intelligence (TAI), 2023.

• [TETCI] Chenru Jiang, Kaizhu Huang, Shufei Zhang, Xinheng Wang, Jimin Xiao, Zhenxing Niu, Amir Hussain: Towards Simple and Accurate Human Pose Estimation With Stair Network.IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) 7(3): 805-817, 2023.

• [TETCI] Kai Yao, Kaizhu Huang, Jie Sun, Amir Hussain, Curran Jude: PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2023.

• [TETCI] Yangfan Zhou, Kaizhu Huang, Cheng, Cheng; Wang, Xuguang; Hussain, Amir; Liu, Xin,Towards Faster Training Algorithms Exploiting Bandit Sampling from Convex to Strongly Convex Conditions, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2023

• [TOMM] Yijie Hu, Bing Dong, Kaizhu Huang, Wei Wang, Xiaowei Huang, Qiu-Feng Wang, Scene Text Recognition via Dual-path Network with Shape-driven Alignment, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2023.

• [JBHI] Zixian Su, Kai Yao,Xi Yang, Jie Sun, Amir Hussain, Kaizhu Huang, Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation, IEEE Journal of Biomedical and Health Informatics (JBHI), 2023.

• [NECO] Guanyu Yang, Kaizhu Huang, Rui Zhang, Xi Yang, Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning, Neural Computation, 2023

• [PR] Zhaorui Tan, Xi Yang, Zihan Ye, Qiufeng Wang, Yuyao Yan, Anh Nguyen, Kaizhu Huang:Semantic Similarity Distance: Towards better text-image consistency metric in text-to-image generation.Pattern Recognition (PR) 144: 109883, 2023

• [NN] Penglei Gao, Xi Yang, Rui Zhang, J.Y. Goulermas, Y. Geng, Y. Yan, Kaizhu Huang, Generalized image outpainting with U-transformer, Neural Networks (NN), 162, 1-10, 2023.

• [INFF] Chenru Jiang, Kaizhu Huang, Junwei Wu, Xinheng Wang, Jimin Xiao, Amir Hussain, PointGS: Bridging and Fusing Geometric and Semantic Space for 3D Point Cloud Analysis, Information Fusion (INFF), 2023.

• [MLJ] Shufei Zhang, Zhuang Qian, Kaizhu Huang, Rui Zhang, Jimin Xiao, Yuan He, Canyi Lu: Robust generative adversarial network. Machine Learning Journal (MLJ) 112(12): 5135-5161, 2023

2022

• [ECCV] Kai Yao, Penglei Gao, Kaizhu Huang, Xi Yang, Jie Sun, and Rui Zhang, Outpainting by Queries, European Conference on Computer Vision (ECCV), 2022.

• [MM] Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, and Rui Zhang, Chaoliang Zhong, Certifying Better Robust Generalization for Unsupervised Domain Adaptation, ACM Multimedia (MM), 2022.

• [TKDE] Penglei Gao, Xi Yang, Kaizhu Huang, Rui Zhang, Yannis Goulermas, Explainable Tensorized Neural Ordinary Differential Equations for Arbitrary-step Time Series Prediction , IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

• [TNNLS] Yangfan Zhou, Kaizhu Huang, Cheng, Cheng; Wang, Xuguang; Hussain, Amir; Liu, Xin, FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity , IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.

• [TMM] Zihan Ye, Fuyuan Hu, Fan Lyu, Linyan Li, Kaizhu Huang, Disentangling Semantic-to-visual Confusion for Zero-shot Learning, IEEE Transactions on Multimedia (TMM), 2022.

• [TOMM] Haotian Xu, Xiaobo Jin, Qiufeng Wang, Amir Hussain, Kaizhu Huang, Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2022.

• [JBHI] Kai Yao, Zixian Su, Kaizhu Huang, Xi Yang, Jie Sun, Amir Hussain, A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation, IEEE Journal of Biomedical and Health Informatics (JBHI), 2022.

• [PR] Xiao-Bo Jin, Jianyu Miao, Qiufeng Wang, Guanggang Geng, Kaizhu Huang, Sparse Matrix Factorization with L21 Norm for Matrix Completion, Pattern Recognition (PR), 2022.

• [PR] D Zheng, J Xiao, Y Wei, Q Wang, Kaizhu Huang, Y Zhao, Unsupervised domain adaptation in homogeneous distance space for person re-identification,Pattern Recognition (PR) 132, 108941, 2022.

• [PR] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Kaizhu Huang, Shan Luo, Yao Zhao, End-to-End Weakly Supervised Semantic Segmentation with Reliable Region Mining, Pattern Recognition (PR), 2022.

• [MLJ] Shufei Zhang, Kaizhu Huang, Zenglin Xu, Re-thinking Model Robustness from Stability: A New Insight to Defend Adversarial Examples, Machine Learning Journal (MLJ), 2022

2021

• [ICCV] Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, and Chaoliang Zhong, Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation, International Conference on Computer Vision (ICCV), 2021.

• [ICML] Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, and Xinping Yi, Towards Better Robust Generalization with Shift Consistency Regularization, International Conference on Machine Learning (ICML), 2021.

• [NeurIPS] Ye Ma, Zixun Lan, Lu Zong, and Kaizhu Huang, Global-aware Beam Search for Neural Abstractive Summarization, Neural Information Processing Systems (NeurIPS), 2021.

• [BMVC] Liuqing Zhao, Fan Lyu, Fuyuan Hu, Fenglei Xu, and Kaizhu Huang, Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing, British Machine Vision Conference (BMVC), 2021.

• [ITJ] Qi Chen, Wei Wang, Kaizhu Huang, Frans Coenen, Zero-shot Text Classification via Knowledge Graph Embedding for Social Media Data, IEEE Internet of Things Journal (ITJ), 2021.

• [TNNLS] Dong, Hang; Wang, Wei; Huang, Kaizhu; Coenen, Frans Automated Social Text Annotation with Joint Multi-Label Attention Networks, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.

• [NN] Shufei Zhang, Kaizhu Huang, Jianke Zhu, Yang Liu, Manifold Adversarial Training for Supervised and Semi-supervised Learning, Neural Networks (NN), 2021.

• [ESWA] Qi Chen, Wei Wang, Kaizhu Huang, Suparna De Frans Coenen, Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities, Expert Systems with Applications (ESWA), 2021.

2020

• [MM] Chenru Jiang, Kaizhu Huang, Shufei Zhang, Henry Wang, and Jimin Xiao, Pay Attention Selectively and Comprehensively: Pyramid Gating Network for Human Pose Estimation without Pretraining, ACM Multimedia (MM), 2020.

• [ECML] Guanyu Yang, Kaizhu Huang, Rui Zhang, John Goulermas, and Amir Hussain, Inductive Generalized Zero-shot Learning with Adversarial Relation Network, European Conference on Machine Learning (ECML), 2020.

• [ECCV] Yanchun Xie, Jimin XIAO, Mingjie Sun, Chao Yao, and Kaizhu Huang, Matching Representations Matters: End-to-End Learning for Neural Texture Transfer, European Conference on Computer Vision (ECCV), 2020.

• [AAAI] Jiezhu Cheng, Kaizhu Huang, and Zibin Zheng, Towards Better Forecasting by Fusing Near and Distant Future Visions, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

• [AAAI] Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang, Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

• [PR] Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang, Generative Adversarial Classifier for Handwriting Characters Super-Resolution, Pattern Recognition (PR), 2020.

• [ESWA] Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Novel Artificial Immune Networks-based Optimization of Shallow Machine Learning (ML) Classifiers, Expert Systems with Applications (ESWA), 2020. 

• [NN] Fangzhou Xiong, Zhiyong Liu, Kaizhu Huang, Xu Yang, and Amir Hussain, Encoding Primitives Generation Policy Learning for Robotic Arm to Overcome Catastrophic Forgetting in Sequential Multi-tasks Learning, Neural Networks (NN), 2020.

• [IS] Hang Dong, Wei Wang, Kaizhu Huang, Frans Coenen, Knowledge Base Enrichment by Relation Learning from Social Tagging Data, Information Sciences (IS), 526: 203-220, 2020. 

• [NN] Kaizhu Huang, Shufei Zhang, Rui Zhang, Amir Hussain, Novel deep neural network based pattern field classification architectures, Neural Networks (NN), 2020.

• [NN] inxuan Sun, Guoqiang Zhong, Yang Chen, Yongbin Liu, Tao Li, Kaizhu Huang, Generative Adversarial Networks with Mixture of t-Distributions Noise for Diverse Image Generation, Neural Networks (NN), 2020.

• [NN] Guoqiang Zhong, Yang Chen, Kaizhu Huang, Generative Adversarial Networks with Decoder-Encoder Output Noises, Neural Networks (NN), 2020.

2019

• [ICCVW] Xiao-Bo Jin, Guo-Sen Xie, Kaizhu Huang, Jianyu Miao, and Qiufeng Wang, Beyond Attributes: High-order Attribute Features for Zero-shot Learning, In International Conference on Computer Vision Workshop (ICCV-W), 2019.

• [ICDM] Shufei Zhang, Kaizhu Huang, Rui Zhang, and Amir Hussain, Generalized Adversarial Training in Riemannian Space , In IEEE Fifteen Conference on Data Mining (ICDM), 2019.

• [ICDM] Xi Yang, Yuyao Yan, Kaizhu Huang, and Rui Zhang, VSB-DVM: An End-to-end Bayesian Nonparametric Generalization of Deep Variational Mixture Model, In IEEE Fifteen Conference on Data Mining (ICDM), 2019.

• [NAACL] Hang Dong, Wei Wang, Kaizhu Huang, and Frans Coenen, Joint Multi-Label Attention Networks for Social Text Annotation, In Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) , 2019 .

• [TNNLS] Xiaobo Jin, Xu-Yao Zhang, Kaizhu Huang, Guanggang Geng, Stochastic Conjugate Gradient Algorithm with Variance Reduction, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.

• [TCSVT] Yanchun Xie, Jimin Xiao, Kaizhu Huang, Jeyarajan Thiyagalingam, Yao Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019.

• [BOOK] Kaizhu Huang, Amir Hussain, Qiufeng Wang, Rui Zhang (eds.),Deep Learning: Fundamentals, Theory, and Applications, Springer, ISBN 978-3-030-06072-5, 2019.

2018

• [TIP] Changzhi Luo, Meng Wang, Kaizhu Huang, Jiashi Feng, Zero-Shot Learning via Attribute Regression and Class Prototype Rectification, IEEE Transactions on Image Processing (TIP), 27(2):637-648, 2018. 

• [TCYB-SYS] Fanzhou Xiong, Biao Sun, Xu Yang, Kaizhu Huang, Hong Qiao, Amir Hussain, Zhi-Yong Liu,Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems (T-CYB-SYS), Pages 1-11, issues 99,2018.

• [TETCI] Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain, Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2018.

• [TIA] Jieming Ma, Haochuan Jiang, Ziqiang Bi, Kaizhu Huang et al. , Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios, IEEE Transactions on Industry Applications (TIA), 2018.

• [INFF] Xu Yang, Lu Zhang, Zhiyong Liu, Shifeng Zhang, Kaizhu Huang, Amir Hussain, Hong Qiao Cross-Modality Interactive Attention Network for Multispectral Pedestrian Detection, Information Fusion (IF), 2018. 

• [NN] Jianyu Sun, Guoqiang Zhong, Kaizhu Huang, Junyu Dong, Banzhaf Random Forests: Cooperative Game Theory Based Random Forests with Consistency, Neural Networks (NN), 2018.

• [PR] Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, Jiashi Feng: IAN: The Individual Aggregation Network for Person Search, Pattern Recognition (PR), 2018.

• [IS] Xiao-Bo Jin, Guang-Gang Geng, Guo-Sen Xie, Kaizhu Huang, Pair-wise Loss for Optimizing NDCG Approximately, Information Sciences (IS), Volume 453, Pages 50-65, 2018. 

• [ESWA] Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction‒ A Novel Technique, Expert Systems With Applications (ESWA), Pages 388-400, Vol. 112, 2018. 

• [BOOK] Guoqiang Zhong and Kaizhu Huang (eds.), Semi-Supervised Learning: Background, Applications and Future Directions, Nova Science Publishers, Inc., 978-1-53613-556-5, 2018.

2017

• [TETCI] Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang, Rui Zhang, Field Support Vector Machines, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 1(6), 454-463, 2017.

• [TCS] Jieming Ma, Haochuan Jiang, Kaizhu Huang, Ziqiang Bi, Kalok Man, Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance, IEEE Transactions on Circuits and Systems I (TCS), 64(12): 3183-3191, 2017. 

2016

• [PR] Yao Lu, Kaizhu Huang, Cheng-Lin Liu, Doubly Stochastic Projected Fixed-Point Algorithm for Large Graph Matching, Pattern Recognition (PR), Vol. 60, 971-982, 2016.

2015

• [ICDM] Chunchun Lv, Kaizhu Huang, and Hai-Ning Liang, A Unified Gradient Regularization Family for Adversarial Examples, In IEEE Fifteen Conference on Data Mining (ICDM) , 2015.

• [WSDM] Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Scalable Data Analytics: Theory and Applications, in Eighth ACM International Conference on Web Search and Data Mining (WSDM), 2015.

• [TNNLS] Yan-Ming Zhang, Kaizhu Huang, Cheng-Lin Liu, MTC: A Fast and Robust Graph-based Transductive Learning Algorithm, IEEE Transactions on Learning Systems and Neural Networks (TNNLS), 26(9): 1979-1991, 2015. 

• [NN]Haiqin Yang, Kaizhu Huang, Irwin King, Michael R. Lyu, Maximum Margin Semi-supervised Learning with Irrelevant Data, Neural Networks (NN), 70: 90-102, 2015. 

2014

• [TPAMI] Xu-Cheng Yin, Kuang Yin, Kaizhu Huang, Hong-Wei Hao, Robust Text Detection in Natural Images, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 36(5): 970-983, 2014. 

• [TCYB] Yan-Ming Zhang, Kaizhu Huang, Xinwen Hou, and Cheng-Lin Liu, Learning Locality Preserving Graph from Data, IEEE Trans. Cybnetics (T-CYB), 44(11): 2088-2098, 2014. 

• [IEEE-SPL] Xu-Yao Zhang, Peipei Yang, Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu, Combination of Classification and Clustering Results with Label Propagation, IEEE Signal Processing Letters (SPL), 21(5): 610-614, 2014. 

• [INFF] Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao, Convex Ensemble Learning with Sparsity and Diversity, Information Fusion (IF), 2014.

• [BOOK] Irwin King, Kaizhu Huang, Heike Sichtig (eds.), Part C: Machine Learning Methods, Handbook of Bio- and Neuroinformatics, Springer, 2014.

• [BOOK] Chris Brown, Heike Sichtig, Irwin King, Kaizhu Huang, Francesco Masulli (eds.), Part D: Modeling Regulatory Networks: The Systems Biology Approach, Handbook of Bio- and Neuroinformatics, Springer, 2014.

• [BOOK] Chu Kiong Loo, Keem SiahYap, KokWai Wong, Andrew Teoh, Kaizhu Huang (eds.), Proceedings of Neural Information Processing, 21st International Conference, Part I-II-III, Lecture Notes on Computer Science 8834-8836, Springer, 2014.

2013

• [ICDM] XuYao Zhang, Kaizhu Huang, and Chenglin Liu, Feature Transformation with Class Conditional Decorrelation. In IEEE Thirteen Conference on Data Mining (ICDM), pages 887-896, 2013.

• [ECML] YanMing Zhang, Kaizhu Huang, Guanggang Geng, and Cheng-Lin Liu, Fast kNN Graph Construction with Locality Sensitive Hashing, In European conference on Machine Learning (ECML), 2013.

• [SIGIR] XuCheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao, Accurate and Robust Text Detection: A Step-In for Text Retrieval in Natural Scene Images, ACM Special Interest Group on Information Retrieval (SIGIR), 2013.

• [MLJ] Peipei Yang, Kaizhu Huang, Cheng-lin Liu, Geometry Preserving Multi-task Metric Learning, Machine Learning Journal (MLJ), Volume 92(1), 133-175, 2013.

Before 2013

• [ECML] Peipei Yang, Kaizhu Huang, and Cheng-Lin Liu, Geometry Preserving Multi-task Metric Learning. In European conference on Machine Learning (ECML), LNCS Vol.7523, pp.648-664, 2012

• [NN] Bo Xu, Kaizhu Huang, Cheng-Lin Liu: Maxi-Min Discriminant Analysis via Online Learning. Neural Networks (NN) 34: 56-64, 2012. (JCR Q1)

• [ICDM] Guoqiang Zhong, Kaizhu Huang, and Cheng-Lin Liu, Low Rank Metric Learning with Manifold Regularization. In IEEE Eleventh conference on Data Mining (ICDM), 1266-1271, 2011. 

• [ICDM] YanMing Zhang, Kaizhu Huang, and Cheng-Lin Liu，Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut, IEEE Eleventh conference on Data Mining (ICDM), 952-961, 2011.

• [IJCAI] XuYao Zhang, Kaizhu Huang, and Cheng-Lin Liu, Pattern Field Classification with Style Normalized Transformation, In The International Joint Conference on Artificial Intelligence (IJCAI), 1621-1626, 2011.

• [KAIS] Kaizhu Huang, Yiming Ying, Colin Campbell, Generalized Sparse Metric Learning With Relative Comparisons. Knowledge and Information Systems (KAIS), Volume 28, Issue 1, pages 25-45, 2011. 

• [UAI] Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu Robust Metric Learning with Smooth Optimization , In The 26th Conference on Uncertainty in Artificial Intelligence (UAI), 244-251, 2010.

• [PRL] Kaizhu Huang, Danian Zheng, Jun Sun, Yoshinobu Hotta, Katsuhito Fujimoto, Satoshi Naoi: Sparse learning for support vector classification. Pattern Recognition Letters (PRL) 31(13): 1944-1951, 2010.

• [ICDM] Kaizhu Huang, Yiming Ying, and Colin Campbell, GSML: A Unified Framework for Sparse Metric Learning，IEEE Ninth conference on Data Mining (ICDM), 189-198, 2009.

• [NeurIPS] Yiming Ying, Kaizhu Huang, and Colin Campbell,, Sparse Metric Learning via Smooth Optimization, in Proc. Advances in Neural Information Processing System 22 (NeurIPS), Cambridge, MA, 2009.

• [NECO] Kaizhu Huang, Danian Zheng, Irwin King, Michael R. Lyu, Arbitrary Norm Support Vector Machines. Neural Computation, Vol. 21, No. 2: 560–582, 200BM9. 

• [BMC-Bio] Yiming Ying, Kaizhu Huang, Colin Campbell, Enhanced Protein Fold Recognition through a Novel Data Integration Approach. BMC Bioinformatics (BMC-Bio), Vol.10:267, 2009. 

• [NN] Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, Michael R. Lyu: A novel kernel-based maximum a posteriori classification method. Neural Networks (NN) 22(7): 977-987, 2009. 

• [CIKM] Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu, Semi-supervised Text Categorization by Active Search, In ACM 17th Conference on Information and Knowledge Management (CIKM), 1517-1518, 2008.

• [ICDM] Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu, Semi-supervised Learning from General Unlabeled Data, In IEEE Eighth conference on Data Mining (ICDM), 273-282, 2008.

• [ICDM] Kaizhu Huang, Irwin King, and Michael R. Lyu, Direct Zero-norm Optimization for Feature Selection, In IEEE Eighth Conference on Data Mining (ICDM), 845-850, 2008.

• [TNNLS] Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. M4: Learning Large Margin Machines Locally and Globally, IEEE Trans. Neural Networks (TNNLS), vol. 19, iss. 2, pp. 260-272, 2008. 

• [BOOK] Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. Machine Learning: Modeling Data Locally and Globally, Springer Verlag ISBN-13: 978-3540794516, 2008.

• [TBE] Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine. IEEE Trans. Biomedical Engineering (TBE), Vol 53, Issue 5, 821- 831, May 2006. 

• [TCYB] Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Imbalanced Learning With Biased Minimax Probability Machine. IEEE Trans. System Man, Cybnetics (T-CYB), Part B, Vol 36, No 4, 913 – 923, August, 2006. 

• [CVPR] Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 558-563, Washington, DC, June 27 -July 2, 2004.

• [ICML] Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Learning Large Margin Machines Locally and Globally. Proceedings international Conference on Machine Learning (ICML), Banff, Canada, pp. 401-408, 2004.

• [JMLR] Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan, The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research (JMLR), Vol. 5, pp. 1253-1286, October 2004. 

1. Zhaorui Tan, Xi Yang, Kaizhu Huang, Rethinking Multi-domain Generalization with A General Learning Objective, The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) , 2024.

2. Zhaorui Tan, Xi Yang, Kaizhu Huang, Semantic-aware Data Augmentation for Text-to-image Synthesis,  Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.

3. Zixian Su, Jingwei Guo, Kai Yao, Xi Yang, Qiu-Feng Wang, Kaizhu Huang, Unraveling Batch Normalization for Realistic Test-Time Adaptation, Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024 (Oral).

4. Zihao Zhou, Qiufeng Wang, Mingyu Jin, Jie Yao, Jianan Ye, Wei Liu, Wei Wang, Xiaowei Huang, Kaizhu Huang, MathAttack: Attacking Large Language Models towards Math Solving Ability, Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.

5.   Wenhui Wei, Jiantao Li, Kaizhu Huang, Jiadong Li, Xin Liu, Yangfan Zhou,  Lite-SVO: Towards A Lightweight Self-Supervised Semantic Visual Odometry Exploiting Multi-Feature Sharing Architecture, 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024.

6. Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye, Xi Yang, and Kaizhu Huang, Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization, International Conference on Computer Vision (ICCV), 2023. 

7. Zhiqiang Gao, Kaizhu Huang, Rui Zhang, Dawei Liu, and Jieming Ma, Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation, International Conference on Computer Vision (ICCV), 2023.

8. Jingwei Guo, Kaizhu Huang, Xinping Yi, and Rui Zhang, Graph Neural Networks with Diverse Spectral Filtering, ACM Web Conference (WWW), 2023.

9. Yiming Lin, Xiaobo Jin, Qiufeng Wang, and Kaizhu Huang, Context Does Matter: End-to-end Panoptic Narrative Grounding with Deformable Attention Refined Matching Network , In IEEE International Conference on Data Mining (ICDM), 2023.

10. Maizhen Ning, Qiufeng Wang, Kaizhu Huang, and Xiaowei Huang, A Symbolic Character-Aware Model for Solving Geometry Problems, ACM Multimedia (MM), 2023.

11. Kai Yao, Zixian Su, Xi Yang, Jie Sun, and Kaizhu Huang, Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation, In ACM Conference on Information and Knowledge Management (CIKM), 2023.

12. Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei huang, and Kaizhu Huang, Learning by Analogy: Diverse Questions Generation in Math Word Problem，Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2023.

13. Yuqi Wang, Wei Wang, Qi Chen, Kaizhu Huang, Anh Nguyen, Suparna De: Prompt-based Zero-shot Text Classification with Conceptual Knowledge. AProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) Student: 30-38, 2023 

14. Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang, Jie Sun, and Kaizhu Huang, Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.

15. Shuyi Qu, Zhenxing Niu, Jianke Zhu, Bin Dong, Kaizhu Huang: Structure First Detail Next: Image Inpainting with Pyramid Generator. ICME: 1265-1270, 2023.

16. Kai Yao, Penglei Gao, Kaizhu Huang, Xi Yang, Jie Sun, and Rui Zhang, Outpainting by Queries, European Conference on Computer Vision (ECCV), 2022.

17. Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, and Rui Zhang, Chaoliang Zhong, Certifying Better Robust Generalization for Unsupervised Domain Adaptation, ACM Multimedia (MM), 2022.

18. Ye Ma, Zixun Lan, Lu Zong, and Kaizhu Huang, Global-aware Beam Search for Neural Abstractive Summarization, Neural Information Processing Systems (NeurIPS), 2021.

19. Liuqing Zhao, Fan Lyu, Fuyuan Hu, Fenglei Xu, and Kaizhu Huang, Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing, British Machine Vision Conference (BMVC), 2021.

20. Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, and Chaoliang Zhong, Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation, International Conference on Computer Vision (ICCV), 2021.

21. Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, and Xinping Yi, Towards Better Robust Generalization with Shift Consistency Regularization, International Conference on Machine Learning (ICML), 2021.

22. Chenru Jiang, Kaizhu Huang, Shufei Zhang, Henry Wang, and Jimin Xiao, Pay Attention Selectively and Comprehensively: Pyramid Gating Network for Human Pose Estimation without Pretraining, ACM Multimedia (MM), 2020.

23. Guanyu Yang, Kaizhu Huang, Rui Zhang, John Goulermas, and Amir Hussain, Inductive Generalized Zero-shot Learning with Adversarial Relation Network, European Conference on Machine Learning (ECML), 2020.

24. Yanchun Xie, Jimin XIAO, Mingjie Sun, Chao Yao, and Kaizhu Huang, Matching Representations Matters: End-to-End Learning for Neural Texture Transfer, European Conference on Computer Vision (ECCV), 2020.

25. Jiezhu Cheng, Kaizhu Huang, and Zibin Zheng, Towards Better Forecasting by Fusing Near and Distant Future Visions, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

26. Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, and Kaizhu Huang, Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.

27. Xiao-Bo Jin, Guo-Sen Xie, Kaizhu Huang, Jianyu Miao, and Qiufeng Wang, Beyond Attributes: High-order Attribute Features for Zero-shot Learning, In International Conference on Computer Vision Workshop (ICCV-W), 2019.

28. Shufei Zhang, Kaizhu Huang, Rui Zhang, and Amir Hussain, Generalized Adversarial Training in Riemannian Space , In IEEE Fifteen Conference on Data Mining (ICDM), 2019.

29. Xi Yang, Yuyao Yan, Kaizhu Huang, and Rui Zhang, VSB-DVM: An End-to-end Bayesian Nonparametric Generalization of Deep Variational Mixture Model, In IEEE Fifteen Conference on Data Mining (ICDM), 2019.

30. Hang Dong, Wei Wang, Kaizhu Huang, and Frans Coenen, Joint Multi-Label Attention Networks for Social Text Annotation, In Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) , 2019 .

31. Chunchun Lv, Kaizhu Huang, and Hai-Ning Liang, A Unified Gradient Regularization Family for Adversarial Examples, In IEEE Fifteen Conference on Data Mining (ICDM) , 2015.

32. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Scalable Data Analytics: Theory and Applications, in Eighth ACM International Conference on Web Search and Data Mining (WSDM), 2015.

33. XuYao Zhang, Kaizhu Huang, and Chenglin Liu, Feature Transformation with Class Conditional Decorrelation. In IEEE Thirteen Conference on Data Mining (ICDM), pages 887-896, 2013.

34. YanMing Zhang, Kaizhu Huang, Guanggang Geng, and Cheng-Lin Liu, Fast kNN Graph Construction with Locality Sensitive Hashing, In European conference on Machine Learning (ECML), 2013.

35. XuCheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao, Accurate and Robust Text Detection: A Step-In for Text Retrieval in Natural Scene Images, ACM Special Interest Group on Information Retrieval (SIGIR), 2013.

36. Peipei Yang, Kaizhu Huang, and Cheng-Lin Liu, Geometry Preserving Multi-task Metric Learning. In European conference on Machine Learning (ECML), LNCS Vol.7523, pp.648-664, 2012

37. Guoqiang Zhong, Kaizhu Huang, and Cheng-Lin Liu, Low Rank Metric Learning with Manifold Regularization. In IEEE Eleventh conference on Data Mining (ICDM), 1266-1271, 2011. '

38. YanMing Zhang, Kaizhu Huang, and Cheng-Lin Liu，Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut, IEEE Eleventh conference on Data Mining (ICDM), 952-961, 2011.

39. XuYao Zhang, Kaizhu Huang, and Cheng-Lin Liu, Pattern Field Classification with Style Normalized Transformation, In The International Joint Conference on Artificial Intelligence (IJCAI), 1621-1626, 2011.

40. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu Robust Metric Learning with Smooth Optimization , In The 26th Conference on Uncertainty in Artificial Intelligence (UAI), 244-251,2010.

41. Kaizhu Huang, Yiming Ying, and Colin Campbell, GSML: A Unified Framework for Sparse Metric Learning，IEEE Ninth conference on Data Mining (ICDM), 189-198, 2009.

42. Yiming Ying, Kaizhu Huang, and Colin Campbell,, Sparse Metric Learning via Smooth Optimization, in Proc. Advances in Neural Information Processing System 22 (NeurIPS), Cambridge, MA, 2009.

43. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu, Semi-supervised Text Categorization by Active Search, In ACM 17th Conference on Information and Knowledge Management (CIKM), 1517-1518, 2008.

44. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu, Semi-supervised Learning from General Unlabeled Data, In IEEE Eighth conference on Data Mining (ICDM), 273-282, 2008.

45. Kaizhu Huang, Irwin King, and Michael R. Lyu, Direct Zero-norm Optimization for Feature Selection, In IEEE Eighth Conference on Data Mining (ICDM), 845-850, 2008.

46. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 558-563, Washington, DC, June 27 -July 2, 2004.

47. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Learning Large Margin Machines Locally and Globally. Proceedings international Conference on Machine Learning (ICML), Banff, Canada, pp. 401-408, 2004.

1. Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Bin Gu, Huan Xiong, Xinping Yi, Inter-feature Relationship Certifies Robust Generalization of Adversarial Training, International Journal of Computer Vision (IJCV), 2024

2. Jiezhu Cheng, Kaizhu Huang, Zibin Zheng, Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training, ACM Transactions on Information System (TOIS), 2024

3. Penglei Gao,  Xi Yang,  Rui Zhang, Ping Guo, Kaizhu Huang, EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables, IEEE Transactions on Cybernetics (T-CYB), 2024.

4. Penglei Gao,  Xi Yang,  Rui Zhang, Kaizhu Huang, Continuous Image Outpainting with Neural ODE, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2024. 

5.  Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, X Yi, B Gu, H Xiong, Perturbation diversity certificates robust generalization, Neural Networks (NN), 106117, 2024.

6. Jianan Ye, Yijie Hu, Xi Yang, Qiu-Feng Wang, Kaizhu Huang, SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection, Pattern Recognition (PR), 2024.

7. Zihan Ye, Guanyu Yang, Xiao-Bo Jin, Youfa Liu, Kaizhu Huang, Rebalanced Zero-shot Learning, IEEE Trans. Image Processing (TIP), 2023.

8. Wenhui Wei, Yangfan Zhou, Kaizhu Huang, Xin Liu, GSL-VO: A Geometric-Semantic Information Enhanced Lightweight Visual Odometry in Dynamic Environments, IEEE Transactions on Instrumentation & Measurement (TIM), 2023.

9. Jiezhu Chen, Kaizhu Huang, Zibing Zheng, Fitting Imbalanced Uncertainties in Multi-Output Time Series Forecasting，ACM Transactions on Knowledge Discovery from Data (TKDD), 2023.

10. Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang, Learning Disentangled Graph Convolutional Networks Locally and Globally, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

11. Li, P; Xiao, Z; Wang, X; Kaizhu Huang, Yi Huang; Gao, H, EPtask: Deep Reinforcement Learning based Energy-efficient and Priority-aware Task Scheduling for Dynamic Vehicular Edge Computing, IEEE Transactions on Intelligent Vehicles (TIV), 2023.

12. Wei Li, C. Chen, Kaizhu Huang, Absorb and Repel: Pseudo-Label Refinement for Intra-Camera Supervised Person Re-identification, IEEE Transactions on Artificial Intelligence (TAI), 2023.

13. Yangfan Zhou, Kaizhu Huang, Cheng, Cheng; Wang, Xuguang; Hussain, Amir; Liu, Xin,Towards Faster Training Algorithms Exploiting Bandit Sampling from Convex to Strongly Convex Conditions, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2023

14. Chenru Jiang, Kaizhu Huang, Shufei Zhang, Xinheng Wang, Jimin Xiao, Zhenxing Niu, Amir Hussain: Towards Simple and Accurate Human Pose Estimation With Stair Network.IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) 7(3): 805-817, 2023.

15. Kai Yao, Kaizhu Huang, Jie Sun, Amir Hussain, Curran Jude: PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2023.

16. Wang, Kaishi; Ma, Jieming; Man, Ka Lok; Kaizhu Huang; Xiaowei Huang,  Sim-to-Real Global Maximum Power Point Tracking with Domain Randomization and Adaptation for Photovoltaic Systems, IEEE Journal of Emerging and Selected Topics in Industrial Electronics (JESTIE), 2023.

17. Yijie Hu, Bing Dong, Kaizhu Huang, Wei Wang, Xiaowei Huang, Qiu-Feng Wang, Scene Text Recognition via Dual-path Network with Shape-driven Alignment, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2023.

18. Zixian Su, Kai Yao,Xi Yang, Jie Sun, Amir Hussain, Kaizhu Huang, Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation, IEEE Journal of Biomedical and Health Informatics (JBHI), 2023.

19. Guanyu Yang, Kaizhu Huang, Rui Zhang, Xi Yang, Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning, Neural Computation, 2023

20. Zhaorui Tan, Xi Yang, Zihan Ye, Qiufeng Wang, Yuyao Yan, Anh Nguyen, Kaizhu Huang:Semantic Similarity Distance: Towards better text-image consistency metric in text-to-image generation.Pattern Recognition (PR) 144: 109883, 2023

21. Jing Li, Q.-F. Wang, Kaizhu Huang, Xi Yang, Rui Zhang, JY Goulermas, Towards better long-tailed oracle character recognition with adversarial data augmentation, Pattern Recognition (PR) 140, 109534, 2023.

22. Chenru Jiang, Kaizhu Huang, Shufei Zhang, Xinheng Wang, Jimin Xiao，Aggregated Pyramid Gating Network for Human Pose Estimation without Pre-training，Pattern Recognition (PR), 2023.

23. Hao Chen, Linyan Li, Fuyuan Hu, Fan Lyu, Liuqing Zhao, Kaizhu Huang, Wei Feng, Zhenping Xia: Multi-semantic hypergraph neural network for effective few-shot learning. Pattern Recognition (PR) 142: 109677,2023.

24. Penglei Gao, Xi Yang, Rui Zhang, J.Y. Goulermas, Y. Geng, Y. Yan, Kaizhu Huang, Generalized image outpainting with U-transformer, Neural Networks (NN), 162, 1-10, 2023.

25. Shufei Zhang, Zhuang Qian, Kaizhu Huang, Rui Zhang, Jimin Xiao, Yuan He, Canyi Lu: Robust generative adversarial network. Machine Learning (MLJ) 112(12): 5135-5161, 2023

26. Y. Wang, W. Wang, Q. Chen, Kaizhu Huang, A Nguyen, S De, A Hussain, Fusing external knowledge resources for natural language understanding techniques: A survey, Information Fusion (IF), 2023.

27. M Tanveer, MA Ganaie, I Beheshti, T Goel, N Ahmad, KT Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin, Deep learning for brain age estimation: A systematic review, Information Fusion (IF), 2023.

28. Chenru Jiang, Kaizhu Huang, Junwei Wu, Xinheng Wang, Jimin Xiao, Amir Hussain, PointGS: Bridging and Fusing Geometric and Semantic Space for 3D Point Cloud Analysis, Information Fusion (IF), 2023.

29. D Zheng, J Xiao, Y Wei, Q Wang, Kaizhu Huang, Y Zhao, Unsupervised domain adaptation in homogeneous distance space for person re-identification,Pattern Recognition (PR) 132, 108941, 2022.

30. Penglei Gao, Xi Yang, Kaizhu Huang, Rui Zhang, Yannis Goulermas, Explainable Tensorized Neural Ordinary Differential Equations for Arbitrary-step Time Series Prediction , IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.

31. Yangfan Zhou, Kaizhu Huang, Cheng, Cheng; Wang, Xuguang; Hussain, Amir; Liu, Xin, FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity , IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.

32. Haotian Xu, Xiaobo Jin, Qiufeng Wang, Amir Hussain, Kaizhu Huang, Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition, ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2022.

33. Kai Yao, Zixian Su, Kaizhu Huang, Xi Yang, Jie Sun, Amir Hussain, A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation, IEEE Journal of Biomedical and Health Informatics (JBHI), 2022.

34. Xiao-Bo Jin, Jianyu Miao, Qiufeng Wang, Guanggang Geng, Kaizhu Huang, Sparse Matrix Factorization with L21 Norm for Matrix Completion, Pattern Recognition (PR), 2022.

35. Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Kaizhu Huang, Shan Luo, Yao Zhao, End-to-End Weakly Supervised Semantic Segmentation with Reliable Region Mining, Pattern Recognition (PR), 2022.

36. Shufei Zhang, Kaizhu Huang, Zenglin Xu, Re-thinking Model Robustness from Stability: A New Insight to Defend Adversarial Examples, Machine Learning (MLJ), 2022

37. Zihan Ye, Fuyuan Hu, Fan Lyu, Linyan Li, Kaizhu Huang, Disentangling Semantic-to-visual Confusion for Zero-shot Learning, IEEE Transactions on Multimedia (TMM), 2022.

38. Qi Chen, Wei Wang, Kaizhu Huang, Frans Coenen, Zero-shot Text Classification via Knowledge Graph Embedding for Social Media Data, IEEE Internet of Things Journal (ITJ), 2021.

39. Dong, Hang; Wang, Wei; Huang, Kaizhu; Coenen, Frans Automated Social Text Annotation with Joint Multi-Label Attention Networks, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.

40. Shufei Zhang, Kaizhu Huang, Jianke Zhu, Yang Liu, Manifold Adversarial Training for Supervised and Semi-supervised Learning, Neural Networks (NN), 2021.

41. Qi Chen, Wei Wang, Kaizhu Huang, Suparna De Frans Coenen, Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities, Expert Systems with Applications (ESWA), 2021.

42. Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang, Generative Adversarial Classifier for Handwriting Characters Super-Resolution, Pattern Recognition (PR), 2020.

43. Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Novel Artificial Immune Networks-based Optimization of Shallow Machine Learning (ML) Classifiers, Expert Systems with Applications (ESWA), 2020. 

44. Fangzhou Xiong, Zhiyong Liu, Kaizhu Huang, Xu Yang, and Amir Hussain, Encoding Primitives Generation Policy Learning for Robotic Arm to Overcome Catastrophic Forgetting in Sequential Multi-tasks Learning, Neural Networks (NN), 2020.

45. Hang Dong, Wei Wang, Kaizhu Huang, Frans Coenen, Knowledge Base Enrichment by Relation Learning from Social Tagging Data, Information Sciences (IS), 526: 203-220, 2020. 

46. Kaizhu Huang, Shufei Zhang, Rui Zhang, Amir Hussain, Novel deep neural network based pattern field classification architectures, Neural Networks (NN), 2020.

47. Jinxuan Sun, Guoqiang Zhong, Yang Chen, Yongbin Liu, Tao Li, Kaizhu Huang, Generative Adversarial Networks with Mixture of t-Distributions Noise for Diverse Image Generation, Neural Networks (NN), 2020.

48. Guoqiang Zhong, Yang Chen, Kaizhu Huang, Generative Adversarial Networks with Decoder-Encoder Output Noises, Neural Networks (NN), 2020.

49. Xiaobo Jin, Xu-Yao Zhang, Kaizhu Huang, Guanggang Geng, Stochastic Conjugate Gradient Algorithm with Variance Reduction, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.

50. Yanchun Xie, Jimin Xiao, Kaizhu Huang, Jeyarajan Thiyagalingam, Yao Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019.

51. Jieming Ma, Haochuan Jiang, Ziqiang Bi, Kaizhu Huang et al. , Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios, IEEE Transactions on Industry Applications (TIA), 2018.

52. Xu Yang, Lu Zhang, Zhiyong Liu, Shifeng Zhang, Kaizhu Huang, Amir Hussain, Hong Qiao Cross-Modality Interactive Attention Network for Multispectral Pedestrian Detection, Information Fusion (IF), 2018. 

53. Jianyu Sun, Guoqiang Zhong, Kaizhu Huang, Junyu Dong, Banzhaf Random Forests: Cooperative Game Theory Based Random Forests with Consistency, Neural Networks (NN), 2018.

54. Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, Jiashi Feng: IAN: The Individual Aggregation Network for Person Search, Pattern Recognition (PR), 2018.

55. Fanzhou Xiong, Biao Sun, Xu Yang, Kaizhu Huang, Hong Qiao, Amir Hussain, Zhi-Yong Liu,Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems (T-CYB-SYS), Pages 1-11, issues 99,2018.

56. Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction‒ A Novel Technique, Expert Systems With Applications (ESWA), Pages 388-400, Vol. 112, 2018. 

57. Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain, Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 2018.

58. Xiao-Bo Jin, Guang-Gang Geng, Guo-Sen Xie, Kaizhu Huang, Pair-wise Loss for Optimizing NDCG Approximately, Information Sciences (IS), Volume 453, Pages 50-65, 2018. 

59. Changzhi Luo, Meng Wang, Kaizhu Huang, Jiashi Feng, Zero-Shot Learning via Attribute Regression and Class Prototype Rectification, IEEE Transactions on Image Processing (TIP), 27(2):637-648, 2018. 

60. Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang, Rui Zhang, Field Support Vector Machines, IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI), 1(6), 454-463, 2017.

61. Jieming Ma, Haochuan Jiang, Kaizhu Huang, Ziqiang Bi, Kalok Man, Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance, IEEE Transactions on Circuits and Systems I (TCS), 64(12): 3183-3191, 2017. 

62. Yao Lu, Kaizhu Huang, Cheng-Lin Liu, Doubly Stochastic Projected Fixed-Point Algorithm for Large Graph Matching, Pattern Recognition (PR), Vol. 60, 971-982, 2016.

63. Haiqin Yang, Kaizhu Huang, Irwin King, Michael R. Lyu, Maximum Margin Semi-supervised Learning with Irrelevant Data, Neural Networks (NN), 70: 90-102, 2015. 

64. Yan-Ming Zhang, Kaizhu Huang, Cheng-Lin Liu, MTC: A Fast and Robust Graph-based Transductive Learning Algorithm, IEEE Transactions on Learning Systems and Neural Networks (TNNLS), 26(9): 1979-1991, 2015. 

65. Xu-Yao Zhang, Peipei Yang, Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu, Combination of Classification and Clustering Results with Label Propagation, IEEE Signal Processing Letters (SPL), 21(5): 610-614, 2014. 

66. Yan-Ming Zhang, Kaizhu Huang, Xinwen Hou, and Cheng-Lin Liu, Learning Locality Preserving Graph from Data, IEEE Trans. Cybnetics (T-CYB), 44(11): 2088-2098, 2014. 

67. Xu-Cheng Yin, Kuang Yin, Kaizhu Huang, Hong-Wei Hao, Robust Text Detection in Natural Images, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 36(5): 970-983, 2014. 

68. Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao, Convex Ensemble Learning with Sparsity and Diversity, Information Fusion (IF), 2014.

69. Peipei Yang, Kaizhu Huang, Cheng-lin Liu, Geometry Preserving Multi-task Metric Learning, Machine Learning (MLJ), Volume 92(1), 133-175, 2013.

70. Bo Xu, Kaizhu Huang, Cheng-Lin Liu: Maxi-Min Discriminant Analysis via Online Learning. Neural Networks (NN) 34: 56-64, 2012. (JCR Q1)

71. Kaizhu Huang, Yiming Ying, Colin Campbell, Generalized Sparse Metric Learning With Relative Comparisons. Knowledge and Information Systems (KAIS), Volume 28, Issue 1, pages 25-45, 2011. 

72. Kaizhu Huang, Danian Zheng, Jun Sun, Yoshinobu Hotta, Katsuhito Fujimoto, Satoshi Naoi: Sparse learning for support vector classification. Pattern Recognition Letters (PRL) 31(13): 1944-1951, 2010.

73. Kaizhu Huang, Danian Zheng, Irwin King, Michael R. Lyu, Arbitrary Norm Support Vector Machines. Neural Computation, Vol. 21, No. 2: 560–582, 2009. 

74. Yiming Ying, Kaizhu Huang, Colin Campbell, Enhanced Protein Fold Recognition through a Novel Data Integration Approach. BMC Bioinformatics (BMC-Bio), Vol.10:267, 2009. 

75. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, Michael R. Lyu: A novel kernel-based maximum a posteriori classification method. Neural Networks (NN) 22(7): 977-987, 2009. 

76. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. M4: Learning Large Margin Machines Locally and Globally, IEEE Trans. Neural Networks (TNNLS), vol. 19, iss. 2, pp. 260-272, 2008. 

77. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine. IEEE Trans. Biomedical Engineering (TBE), Vol 53, Issue 5, 821- 831, May 2006. 

78. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Imbalanced Learning With Biased Minimax Probability Machine. IEEE Trans. System Man, Cybnetics (T-CYB), Part B, Vol 36, No 4, 913 – 923, August, 2006. 

79. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan, The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research (JMLR), Vol. 5, pp. 1253-1286, October 2004.