Brief Biography

I obtained my bachelor and master degrees from Nanjing University and PhD degree from Hong Kong University of Science and Technology (HKUST). Prior to joining Southern University of Science and Technology (SUSTech), I have worked in HKUST and Hong Kong Baptist University (HKBU) for about eight years.
My current research interests include artificial intelligence, machine learning, pattern recognition, and data mining. I am especially interested in designing learning models based on/for foundation models such as LLMs and MLLMs.

News

  • [July 2026] One paper got accepted by ACMMMM 2026.
  • [May 2026] Three papers got accepted by ICML 2026 and one paper by KDD 2026.
  • [February 2026] Two papers got accepted by CVPR 2026.
  • [January 2026] Three papers got accepted by ICLR 2026.
  • [November 2025] Two papers got accepted by AAAI 2026.
  • [September 2025] One paper got accepted by NeurIPS 2025.
  • [June 2025] One paper got accepted by ICCV 2025.
  • [May 2025] Two papers got accepted by ICML 2025.
  • [February 2025] One paper got accepted by CVPR 2025.
  • [January 2025] Five papers got accepted by ICLR 2025.
  • [March 2022] We have released a package for multi-task learning at Github. Details can be found in a JMLR paper.

    • Contact

  • Outdated email addresses: zhangyu@cse.ust.hk; yuzhang@comp.hkbu.edu.hk; yuzhangcse@ust.hk; yuzhangcse@cse.ust.hk
  • Office: Room 412, South Tower, College of Engineering, SUSTech.

    • Publications

    [Google Scholar] [DBLP]

    Books

  • Qiang Yang, Yu Zhang, Wenyuan Dai, and Sinno Jialin Pan. Transfer Learning. Cambridge University Press, ISBN 9781107016903, 2020. (A Chinese Translation has been published by China Machine Press with ISBN 9787111661283.) (Choice Outstanding Academic Title 2020, Choice Reviews)

    • Refereed Journal Papers

  • Yang Chen, Yanbin Wei, Ke Jin, Yi Kong, James T. Kwok, and Yu Zhang. Alternating Training-based Label Smoothing Enhances Prompt Generalization. To appear in IEEE Transactions on Image Processing (TIP).
  • Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok, and Yu Zhang. Mixture of Cluster-Conditional LoRA Experts for Vision-Language Instruction Tuning. IEEE Transactions on Image Processing (TIP), 35: 3881–3892, 2026..
  • Yanbin Wei, Qiushi Huang, James T. Kwok, and Yu Zhang. KICGPTv2: Large Language Model with Knowledge in Context for Knowledge Graph Completion. IEEE Transactions on Knowledge and Data Engineering (TKDE), 38(7): 4464-4476, 2026.
  • Yang Chen, Shuai Fu, and Yu Zhang. MoPD: Mixture-of-Prompts Distillation for Vision-Language Models. IEEE Transactions on Multimedia (TMM), 28: 1943-1954, 2026.
  • Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu, Ivor W. Tsang, and James T. Kwok. Dual-balancing for Multi-Task Learning. Neural Networks (NN), 195: 108317, 2026.
  • Yuepeng Jiang, Yunhao Gou, Wenbo Zhang, Xuehao Wang, Yu Zhang, and Qiang Yang. Mitigating Scale Imbalance and Conflicting Gradients in Deep Multi-Task Learning. Frontiers of Computer Science (FCS), 20(2): 2002318, 2026.
  • Yulong Zhang, Shuhao Chen, Weisen Jiang, Yu Zhang, Jiangang Lu, and James T. Kwok. Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation. Neural Networks (NN), 184: 107031, 2025.
  • Pengxin Guo, Pengrong Jin, Ziyue Li, Lei Bai, and Yu Zhang. Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting. IEEE Transactions on Intelligent Transportation Systems (TITS), 26(10): 15323–15333, 2025.
  • Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, and Qiang Yang. A Versatile Framework for Unsupervised Domain Adaptation based on Instance Weighting. IEEE Transactions on Image Processing (TIP), 33: 6633-6646, 2024.
  • Feiyang Ye, Baijiong Lin, Zhixiong Yue, Yu Zhang, and Ivor Tsang. Multi-Objective Meta-Learning. Artificial Intelligence (AIJ), 335: 104184, 2024.
  • Shijie Chen, Yu Zhang, and Qiang Yang. Multi-Task Learning in Natural Language Processing: An Overview. ACM Computing Surveys (CSUR), 56(12): 295, 2024.
  • Youzhi Qu, Kai Fu, Linjing Wang, Yu Zhang, Haiyan Wu, and Quanying Liu. Hypergraph-based Multi-Task Feature Selection with Temporally-Constrained Group Sparsity Learning on fMRI. Mathematics, 12(11): 1733, 2024.
  • Qiao Xiao, Yu Zhang, and Qiang Yang. Selective Random Walk for Transfer Learning in Heterogeneous Label Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 46(6): 4476-4488, 2024.
  • Baijiong Lin and Yu Zhang. LibMTL: A Python Library for Multi-Task Learning. Journal of Machine Learning Research (JMLR), 24: 1-7, 2023.
  • Yi Zhang, Yu Zhang, and Wei Wang. Learning Linear and Nonlinear Low-Rank Structure in Multi-Task Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(8): 8157-8170, 2023.
  • Shujun Yang, Yu Zhang, Yao Ding, and Danfeng Hong. Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters (GRSL), 20:5504905, 2023.
  • Yuan Yao, Xutao Li, Yu Zhang, and Yunming Ye. Multi-Source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(4): 2079-2092, 2023. (A technical report is available at arXiv.)
  • Yu Zhang and Qiang Yang. A Survey on Multi-Task Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 34(12): 5586–5609, 2022. (A technical report is available at arXiv.)
  • Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor W. Tsang. Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning. Transactions on Machine Learning Research (TMLR), 2022.
  • Shujun Yang, Yu Zhang, Yuheng Jia, and Weijia Zhang. Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 15: 7741-7754, 2022. (Code)
  • Shuangyin Li, Weiwei Chen, Yu Zhang, Gansen Zhao, Rong Pan, Zhenhua Huang, and Yong Tang. A Context-Enhanced Sentence Representation Learning Method for Close Domains with Topic Modeling. Information Sciences, 607(August): 186–210, 2022.
  • Shuangyin Li, Yu Zhang, and Rong Pan. Bi-Directional Recurrent Attentional Topic Model. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(6): article 74, 2020.
  • Yuan Yao, Yu Zhang, Xutao Li, and Yunming Ye. Discriminative Distribution Alignment: A Unified Framework for Heterogeneous Domain Adaptation. Pattern Recognition (PR), 101: article 107165, 2020.
  • Yuan Yao, Xutao Li, Yunming Ye, Feng Liu, Michael K. Ng, Zhichao Huang, and Yu Zhang. Low-Resolution Image Categorization via Heterogeneous Domain Adaptation. Knowledge-Based Systems (KBS), 163(1): 656–665, 2019.
  • Yu Zhang and Qiang Yang. An Overview of Multi-Task Learning. National Science Review (NSR), 5(1): 30–43, 2018. (A Chinese translation can be found here.) (Best Paper Award)
  • Qing Bao, William K. Cheung, Yu Zhang, and Jiming Liu. A Component-based Diffusion Model with Structural Diversity for Social Networks. IEEE Transactions on Cybernetics (TCYB), 47(4): 1078-1089, 2017.
  • Yu Zhang, William K. Cheung, and Jiming Liu. A Unified Framework for Epidemic Prediction based on Poisson Regression. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 2878-2892, 2015. (Link)
  • Deming Zhai, Yu Zhang, Dit-Yan Yeung, Hong Chang, Xilin Chen, and Wen Gao. Instance-Specific Canonical Correlation Analysis. Neurocomputing, 155(1): 205-218, 2015.
  • Yu Zhang and Dit-Yan Yeung. A Regularization Approach to Learning Task Relationships in Multitask Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 8(3): article 12, 2014. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multilabel Relationship Learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(2): article 7, 2013. (Link)
  • Yu Zhang and Dit-Yan Yeung. Transfer Metric Learning with Semi-Supervised Extension. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3): article 54, 2012. (Link)
  • Yu Zhang and Dit-Yan Yeung. Semisupervised Generalized Discriminant Analysis. IEEE Transactions on Neural Network (TNN), 22(8): 1207-1217, 2011. (Link)
  • Yu Zhang and Zhi-Hua Zhou. A New Age Estimation Method based on Ensemble Learning. ACTA AUTOMATICA SINICA, 34(8): 997-1000, 2008. (in Chinese)
  • Yu Zhang and Zhi-Hua Zhou. Research on Facial Features that Impact Gender Classification. Journal of Jiangsu Polytechnic University, 17: 9–12, 2005. (in Chinese) (Best Paper Award by Jiangsu Province Computer Society 2005)

    • Refereed Conference Papers

  • Yang Chen, Zhan Zhuang, Yanbin Wei, Zebin Chen, Hua Liu, and Yu Zhang. Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models. In: Proceedings of the 34th ACM International Conference on Multimedia (ACMMM), Rio de Janeiro, Brazil, 2026.
  • Shuaidi Wang, Zhan Zhuang, Ruping Huang, and Yu Zhang. NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs. In: Proceedings of the 43rd International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.
  • Shuhao Chen, Weisen Jiang, Yeqi Gong, Shengda Luo, Chengxiang Zhuo, Zang Li, James Kwok, and Yu Zhang. SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance–Diversity Data Selection. In: Proceedings of the 43rd International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.
  • Yuan Yao, Jin Song, Huixia Li, Tongtong Yuan, Jiaqi Wu, and Yu Zhang. Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain. In: Proceedings of the 43rd International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.
  • Zebin Chen, Fei Xing, Yang Chen, Hua Liu, Andy Chow, Yuhua Qian, and Yu Zhang. SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning. In: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jeju, South Korea, 2026.
  • Xuerui Zhang, Xuehao Wang, Zhan Zhuang, Linglan Zhao, Ziyue Li, Xinmin Zhang, Zhihuan Song, and Yu Zhang. HAD: Heterogeneity-Aware Distillation for Lifelong Heterogeneous Learning. In: Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, 2026.
  • Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu, James Kwok, and Yu Zhang. DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs. In: Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, 2026.
  • Yang Chen, Yanbin Wei, James Kwok, and Yu Zhang. Discrete Latent Features Ablate Adversarial Attack: A Robust Prompt Tuning Framework for VLMs. In: Proceedings of the Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026.
  • Xiequn Wang, Zhan Zhuang, Shengda Luo, and Yu Zhang. MoSA: Mosaic Shared Adaptation of Large Language Models. In: Proceedings of the Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026.
  • Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Xin Jin, Zhenguo Li, James Kwok, and Yu Zhang. Reasoning-Aligned Perception Decoupling for Scalable Multi-modal Reasoning. In: Proceedings of the Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026.
  • Chengyu Jiao, Siyin Huang, and Yu Zhang. Embracing Positional Bias in Multiple-Choice Question Answering via Permutation Equivariant Neural Networks. In: Proceedings of The Fortieth AAAI Conference on Artificial Intelligence (AAAI), Singapore, 2026.
  • Hua Liu, Yanbin Wei, Fei Xing, Tyler Derr, Haoyu Han, and Yu Zhang. Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution. In: Proceedings of The Fortieth AAAI Conference on Artificial Intelligence (AAAI), Singapore, 2026.
  • Zhan Zhuang, Xiequn Wang, Zebin Chen, Feiyang Ye, Ying Wei, Kede Ma, and Yu Zhang. One-Token Verification for Reasoning Correctness Estimation. In: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP), Budapest, Hungary, 2026.
  • Yanbin Wei, Chun Kang, Siwei Li, Haoxuan Che, Yang Chen, Hua Liu, Jian Liu, Zhuang Liu, Can Ouyang, Fei Xing, Lei Sha, Rui Liu, Yu Zhang, and James Kwok. HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning. In: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP), Budapest, Hungary, 2026.
  • Yanbin Wei, Yang Chen, Renling Gan, Ziru Liu, Xinyu Fu, Chun Kang, Ning Lu, Rui Liu, Yu Zhang, and James Kwok. VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization. In: Findings of EMNLP, Budapest, Hungary, 2026.
  • Yue Wu, Qinghe Zheng, Yu Zhang, and Jian Huang. CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models. In: Findings of EMNLP, Budapest, Hungary, 2026.
  • Hao Zhang, Zhan Zhuang, Xuehao Wang, Xiaodong Yang, and Yu Zhang. MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition. In: Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, USA, 2025.
  • Yanbin Wei, Xuehao Wang, Zhan Zhuang, Yang Chen, Shuhao Chen, Yulong Zhang, James Kwok, and Yu Zhang. Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction. In: Proceedings of the Forty-Second International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.
  • Zhan Zhuang, Xiequn Wang, Wei Li, Yulong Zhang, Qiushi Huang, Shuhao Chen, Xuehao Wang, Yanbin Wei, Yuhe Nie, Kede Ma, Yu Zhang, and Ying Wei. Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation. In: Proceedings of the Forty-Second International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.
  • Xiequn Wang, Zhan Zhuang, and Yu Zhang. PLAN: Proactive Low-Rank Allocation for Continual Learning. In: Proceedings of the International Conference on Computer Vision (ICCV), Honolulu, USA, 2025.
  • Qiushi Huang, Tom Ko, Zhan Zhuang, Lilian Tang, and Yu Zhang. HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore, 2025. (Oral presentation)
  • Qiushi Huang, Tom Ko, Lilian Tang, and Yu Zhang. ComLoRA: A Competitive Learning Approach for Enhancing LoRA. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore, 2025.
  • Xuehao Wang, Zhan Zhuang, Feiyang Ye, and Yu Zhang. MTSAM: Multi-Task Fine-Tuning for Segment Anything Model. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore, 2025.
  • Xuehao Wang, Liyuan Wang, Binghuai Lin, and Yu Zhang. HeadMap: Locating and Enhancing Knowledge Circuits in LLMs. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore, 2025.
  • Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, and Ivor Tsang. Sharpness-Aware Black-Box Optimization. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore, 2025.
  • Kai Chen, Yunhao Gou, Runhui Huang, Zhili Liu, Daxin Tan, Jing Xu, Chunwei Wang, Yi Zhu, Yihan Zeng, Kuo Yang, Dingdong WANG, Kun Xiang, Haoyuan Li, Haoli Bai, Jianhua Han, Xiao-Hui Li, Weike Jin, Nian Xie, Yu Zhang, James Kwok, Hengshuang Zhao, Xiaodan Liang, Dit-Yan Yeung, Xiao Chen, Zhenguo Li, Wei Zhang, Qun Liu, Lanqing Hong, Lu Hou, and Hang Xu. EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions. In: Proceedings of The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville TN, USA, 2025.
  • Chengyu Jiao, Shuhao Chen, and Yu Zhang. SPE Attention: Making Attention Equivariant to Semantic-Preserving Permutation for Code Processing. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), Suzhou, China, 2025.
  • Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, and Yu Zhang. Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), Suzhou, China, 2025.
  • Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, and James T. Kwok. Mixture of insighTful Experts (MoTE): The Synergy of Reasoning Chains and Expert Mixtures in Self-Alignment. In: Proceedings of the Sixty-Third Annual Meeting of the Association for Computational Linguistics (ACL), Vienna, Austria, 2025.
  • Zhan Zhuang, Yulong Zhang, Xuehao Wang, Jiangang Lu, Ying Wei, and Yu Zhang. Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models. In: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
  • Yanbin Wei, Shuai Fu, Weisen Jiang, Zejian Zhang, Zhixiong Zeng, Qi Wu, James T. Kwok, and Yu Zhang. GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning. In: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
  • Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, and Yu Zhang. RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models. In: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
  • Yulong Zhang, Yuan Yao, Shuhao Chen, Pengrong Jin, Yu Zhang, Jian Jin, and Jiangang Lu. Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective. In: Proceedings of the Forty-First International Conference on Machine Learning (ICML), Vienna, Austria, 2024.
  • Zhan Zhuang, Yu Zhang, and Ying Wei. Gradual Domain Adaptation via Gradient Flow. In: Proceedings of the Twelfth International Conference on Learning Representations (ICLR), Vienna, Austria, 2024. (Spotlight)
  • Shuai Fu, Xiequn Wang, Qiushi Huang, and Yu Zhang. Nemesis: Normalizing the Soft-Prompt Vectors of Vision-Language Models. In: Proceedings of the Twelfth International Conference on Learning Representations (ICLR), Vienna, Austria, 2024. (Spotlight)
  • Feiyang Ye, Yueming Lyu, Xuehao Wang, Yu Zhang, and Ivor Tsang. Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning. In: Proceedings of the Twelfth International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.
  • Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. In: Proceedings of the Twelfth International Conference on Learning Representations (ICLR), Vienna, Austria, 2024. (Spotlight)
  • Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, and James T. Kwok. Forward-Backward Reasoning in Large Language Models for Mathematical Verification. In: Findings of ACL, Bangkok, Thailand, 2024.
  • Qiushi Huang, Xubo Liu, Tom Ko, Bo Wu, Wenwu Wang, Yu Zhang, and Lilian Tang. Selective Prompting Tuning for Personalized Conversations with LLMs. In: Findings of ACL, Bangkok, Thailand, 2024.
  • Xuehao Wang, Weisen Jiang, Shuai Fu, and Yu Zhang. Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Vilnius, Republic of Lithuania, 2024.
  • Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok, and Yu Zhang. Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation. In: Proceedings of the Eighteenth European Conference on Computer Vision (ECCV), MiCo Milano, Italy, 2024.
  • Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, and Ying-Cong Chen. MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders. In: Proceedings of the Eighteenth European Conference on Computer Vision (ECCV), MiCo Milano, Italy, 2024.
  • Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, and Ivor W. Tsang. A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization. In: Proceedings of the Fiftieth European Conference on Artificial Intelligence (ECAI), MiCo Milano, Italy, 2024.
  • Qiao Xiao, Jinjing Zhu, Boqian Wu, and Yu Zhang. Open-Set Semi-Supervised Learning by Distribution Alignment. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024.
  • Shaojie Zhang, Yiwei Ding, Enrui Hu, Yu Zhang, and Yue Yu. Enhancing Code Representation Learning for Code Search with Abstract Code Semantics. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024.
  • Weisen Jiang, Yu Zhang, and James T. Kwok. Effective Structured Prompting by Meta-Learning and Representative Verbalizer. In: Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, 2023.
  • Feiyang Ye, Xuehao Wang, Yu Zhang, and Ivor Tsang. Multi-Task Learning via Time-Aware Neural ODE. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2023.
  • Yunhao Gou, Tom Ko, Hansi Yang, James T. Kwok, Yu Zhang, and Mingxuan Wang. Leveraging per Image-Token Consistency for Vision-Language Pre-training. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023.
  • Weisen Jiang, Hansi Yang, Yu Zhang, and James T. Kwok. An Adaptive Policy to Employ Sharpness-Aware Minimization. In: Proceedings of the 11th International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.
  • Zhixiong Yue, Yu Zhang, and Jie Liang. Learning Conflict-Noticed Architecture for Multi-Task Learning. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, USA, 2023.
  • Qiushi Huang, Yu Zhang, Tom Ko, Xubo Liu, Bo Wu, Wenwu Wang, and Lilian Tang. Personalized Dialogue Generation with Persona-Adaptive Attention. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, USA, 2023. (A technical report is available at arXiv.)
  • Qiushi Huang, Shuai Fu, Xubo Liu, Wenwu Wang, Tom Ko, Yu Zhang, and Lilian Tang. Learning Retrieval Augmentation for Personalized Dialogue Generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2023.
  • Yanbin Wei, Qiushi Huang, Yu Zhang, and James T. Kwok. KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. In: Findings of EMNLP, Singapore, 2023.
  • Xiyu Wang, Pengxin Guo, and Yu Zhang. Unsupervised Domain Adaptation via Bidirectional Cross-Attention Transformer. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 2023. (A technical report is available at arXiv.)
  • Feiyang Ye, Jianghan Bao, and Yu Zhang. Partially-Labeled Domain Generalization via Multi-Dimensional Domain Adaptation. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), Queensland, Australia, 2023.
  • Xubo Liu, Qiushi Huang, Xinhao Mei, Haohe Liu, Qiuqiang Kong, Jianyuan Sun, Shengchen Li, Tom Ko, Yu Zhang, Lilian Tang, Mark Plumbley, Volkan Kilic, and Wenwu Wang. Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention. In: Proceedings of the Twenty-Fourth Conference of the International Speech Communication Association (INTERSPEECH), Dublin, Ireland, 2023.
  • Qiao Xiao, Boqian Wu, Yu Zhang, Shiwei Liu, Mykola Pechenizkiy, Elena Mocanu, and Decebal Constantin Mocanu. Dynamic Sparse Network for Time Series Classification: Learning What to "See". In: Proceedings of the Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS/NIPS), New Orleans, City in Louisiana, USA, 2022.
  • Weisen Jiang, James T. Kwok, and Yu Zhang. Subspace Learning for Effective Meta-Learning. In: Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.
  • Junyi Ao, Rui Wang, Long Zhou, Shujie Liu, Shuo Ren, Yu Wu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, and Furu Wei. SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), Dublin, 2022. (A technical report is available at arXiv.) (project)
  • Fengpeng Yue, Yan Deng, Lei He, Tom Ko, and Yu Zhang. Exploring Machine Speech Chain for Domain Adaptation. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, 2022.
  • Rui Wang, Junyi Ao, Long Zhou, Shujie Liu, Zhihua Wei, Tom Ko, Qing Li, and Yu Zhang. Multi-View Self-Attention based Transformer for Speaker Recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, 2022. (A technical report is available at arXiv.)
  • Juan Zha, Zheng Li, Ying Wei, and Yu Zhang. Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering. In: Findings of EMNLP, Abu Dhabi, 2022.
  • Qianqian Dong, Fengpeng Yue, Tom Ko, Mingxuan Wang, Qibing Bai, and Yu Zhang. Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation. In: Proceedings of the 23rd Conference of the International Speech Communication Association (INTERSPEECH), Incheon, Korea, 2022.
  • Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko, and Haizhou Li. LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT. In: Proceedings of the 23rd Conference of the International Speech Communication Association (INTERSPEECH), Incheon, Korea, 2022.
  • Qibing Bai, Tom Ko, and Yu Zhang. A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis. In: Proceedings of the 23rd Conference of the International Speech Communication Association (INTERSPEECH), Incheon, Korea, 2022.
  • Zhixiong Yue, Pengxin Guo, Yu Zhang, and Christy Liang. Learning Feature Alignment Architecture for Domain Adaptation. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022. (An old version is available at arXiv.)
  • Zhixiong Yue, Baijiong Lin, Yu Zhang, and Christy Liang. Effective, Efficient and Robust Neural Architecture Search. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022. (An old version is available at arXiv.)
  • Pengxin Guo, Jinjing Zhu, and Yu Zhang. Selective Partial Domain Adaptation. In: Proceedings of the 33rd British Machine Vision Conference (BMVC), London, UK, 2022.
  • Wenya Zhu, Yinghua Zhang, Yu Zhang, Yuhang Zhou, Yinfu Feng, Qing Da, Anxiang Zeng, and Yuxiang Wu. DHA: Product Title Generation with Discriminative Hierarchical Attention for E-commerce. In: Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Chengdu, China, 2022.
  • Wenbo Zhang, Yunhao Gou, Yuepeng Jiang, and Yu Zhang. Adversarial VAE with Normalizing Flows for Multi-Dimensional Classification. In: Proceedings of the Fifth Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Shenzhen, China, 2022.
  • Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, and Yu Zhang. Multi-Objective Meta Learning. In: Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS/NIPS), 2021. (An old version is available at arXiv.)
  • Weisen Jiang, James T. Kwok, and Yu Zhang. Effective Meta-Regularization by Kernelized Proximal Regularization. In: Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS/NIPS), 2021.
  • Yi Zhang, Yu Zhang, and Wei Wang. Multi-Task Learning via Generalized Tensor Trace Norm. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 2254-2262, 2021. (A technical report is available at arXiv.)
  • Qiao Xiao and Yu Zhang. Distant Transfer Learning via Deep Random Walk. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), pp. 10422–10429, 2021. (A technical report is available at arXiv.)
  • Pengxin Guo, Chang Deng, Linjie Xu, Xiaonan Huang, and Yu Zhang. Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021. (A technical report is available at arXiv.)
  • Ziyang Wang, Yunhao Gou, Jingjing Li, Yu Zhang, and Yang Yang. Region Semantically Aligned Network for Zero-Shot Learning. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.
  • Jianghan Bao and Yu Zhang. Time-Aware Recommender System via Continuous-Time Modeling. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. (Short paper) (code)
  • Weisen Jiang, Yu Zhang, and James T. Kwok. SEEN: Few-Shot Classification with SElf-ENsemble. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), 2021.
  • Ruihong Yang, Junchao Tian, and Yu Zhang. Regularized Mutual Learning for Personalized Federated Learning. In: Proceedings of the 13th Asian Conference on Machine Learning (ACML), 2021.
  • Sicong Liang, Chang Deng, and Yu Zhang. A Simple Approach to Balance Task Loss in Multi-Task Learning. In: Proceedings of the 2021 IEEE International Conference on Big Data (BigData), 2021. (An old version is available at arXiv.)
  • Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying Wei, Yu Zhang, and Qiang Yang. Learn to Cross-lingual Transfer with Meta Graph Learning across Heterogeneous Languages. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2290-2301, 2020.
  • Guang Shen, Riwei Lai, Rui Chen, Yu Zhang, Kejia Zhang, Qilong Han, and Hongtao Song. WISE: Word-Level Interaction-based Multimodal Fusion for Speech Emotion Recognition. In: Proceedings of the 21st Annual Conference of the International Speech Communication Association (InterSpeech), pp. 369-373, Shanghai, China, 2020.
  • Shuangyin Li, Yu Zhang, Rong Pan, and Kaixiang Mo. Adaptive Probabilistic Word Embedding. In: Proceedings of the Web Conference (WWW), pp. 651-661, Taipei, 2020.
  • Yinghua Zhang, Yu Zhang, Ying Wei, Kun Bai, Yangqiu Song, and Qiang Yang. Fisher Deep Domain Adaptation. In: Proceedings of SIAM International Conference on Data Mining (SDM), pp. 469-477, Cincinnati, Ohio, USA, 2020.
  • Yu Zhang and Lei Han. Learning (from) Deep Hierarchical Structure among Features. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), pp. 5837-5844, Honolulu, Hawaii, USA, 2019. (Supplementary Material)
  • Yuan Yao, Yu Zhang, Xutao Li, and Yunming Ye. Heterogeneous Domain Adaptation via Soft Transfer Network. In: Proceedings of the 27th ACM International Conference on Multimedia (MM), pp. 1578-1586, Nice, France, 2019. (A technical report is available at arXiv.)
  • Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, and Xin Li. Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), pp. 4253-4260, Honolulu, Hawaii, USA, 2019. (A technical report is available at arXiv.) (data)
  • Guangneng Hu, Yu Zhang, and Qiang Yang. Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text. In: Proceedings of the Web Conference (WWW), pp. 2822-2829, San Francisco, California, USA, 2019. (A technical report is available at arXiv.)
  • Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, and Qiang Yang. Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4589-4599, Hong Kong, China, 2019. (code)
  • Yinghua Zhang, Yu Zhang, and Qiang Yang. Parameter Transfer Unit for Deep Neural Networks. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 82-95, Macao, China, 2019. (A technical report is available at arXiv.) (Best Paper Award)
  • Dou Huang, Xuan Song, Zipei Fan, Renhe Jiang, Ryosuke Shibasaki, Yu Zhang, Haizhong Wang, and Yugo Kato. A Variational Autoencoder based Generative Model of Urban Human Mobility. In: Proceedings of the 2nd IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 425-430, San Jose, CA, USA, 2019.
  • Yu Zhang, Ying Wei, and Qiang Yang. Learning to Multitask. In: Proceedings of the 32nd Annual Conference on Neural Information Processing Systems (NeurIPS/NIPS), pp. 5776–5787, Montréal, Canada, 2018. (A technical report is available at arXiv.)
  • Ying Wei, Yu Zhang, Junzhou Huang, and Qiang Yang. Transfer Learning via Learning to Transfer. In: Proceedings of the 35th International Conference on Machine Learning (ICML), pp. 5072-5081, Stockholm, Sweden, 2018. (A technical report is available at arXiv.)
  • Kaixiang Mo, Yu Zhang, Shuangyin Li, Jiajun Li, and Qiang Yang. Personalizing a Dialogue System with Transfer Reinforcement Learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 5317-5324, New Orleans, Lousiana, USA, 2018. (A technical report is available at arXiv.)
  • Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, and Qiang Yang. Transferable Contextual Bandit for Cross-Domain Recommendation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 3619-3626, New Orleans, Lousiana, USA, 2018.
  • Zheng Li, Ying Wei, Yu Zhang, and Qiang Yang. Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 5852-5859, New Orleans, Lousiana, USA, 2018. (code)
  • Guangneng Hu, Yu Zhang, and Qiang Yang. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), pp. 667–676, Lingotto, Turin, Italy, 2018. (A technical report is available at arXiv.)
  • Yu Zhang and Yuan Jiang. Multimodal Linear Discriminant Analysis via Structural Sparsity. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3448-3454, Melbourne, Australia, 2017.
  • Zheng Li, Yu Zhang, Ying Wei, Yuxiang Wu, and Qiang Yang. End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2237-2243, Melbourne, Australia, 2017. (code)
  • Bo Liu, Ying Wei, Yu Zhang, and Qiang Yang. Deep Neural Networks for High Dimension, Low Sample Size Data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2287-2293, Melbourne, Australia, 2017.
  • Yu Zhang and Qiang Yang. Learning Sparse Task Relations in Multi-Task Learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 2914-2920, San Francisco, California, USA, 2017.
  • Ben Tan, Yu Zhang, Sinno Jialin Pan, and Qiang Yang. Distant Domain Transfer Learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 2604-2610, San Francisco, California, USA, 2017.
  • Shuangyin Li, Yu Zhang, Rong Pan, Mingzhi Mao, and Yang Yang. Recurrent Attentional Topic Model. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 3223-3229, San Francisco, California, USA, 2017. (Project page)
  • Lei Han, Yu Zhang, and Tong Zhang. Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1585-1594, San Francisco, California, USA, 2016. (The first two authors contributed equally)
  • Lei Han, Yu Zhang, Xiu-Feng Wan, and Tong Zhang. Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 865-874, San Francisco, California, USA, 2016.
  • Shuangyin Li, Rong Pan, Yu Zhang, and Qiang Yang. Correlated Tag Learning in Topic Model. In: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), New York City, NY, USA, 2016.
  • Lei Han and Yu Zhang. Multi-Stage Multi-Task Learning with Reduced Rank. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), pp. 1638-1644, Phoenix, Arizona, USA, 2016. (Both authors contributed equally)
  • Lei Han and Yu Zhang. Reduction Techniques for Graph-based Convex Clustering. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), pp. 1645-1651, Phoenix, Arizona, USA, 2016. (Both authors contributed equally)
  • Yu Zhang. Parallel Multi-Task Learning. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 629-638, Atlantic City, New Jersey, USA, 2015.
  • Lei Han and Yu Zhang. Learning Tree Structure in Multi-Task Learning. In: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 397-406, Sydney, 2015. (Both authors contributed equally) (Matlab Code)
  • Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu. Differentially Private High-Dimensional Data Publishing via Sampling-Based Inference. In: Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 129-138, Sydney, 2015.
  • Yu Zhang. Multi-Task Learning and Algorithmic Stability. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 3181-3187, Austin Texas, USA, 2015. (Supplementary Material)
  • Lei Han and Yu Zhang. Learning Multi-Level Task Groups in Multi-Task Learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 2638-2644, Austin Texas, USA, 2015. (Both authors contributed equally) (Matlab Code)
  • Lei Han and Yu Zhang. Discriminative Feature Grouping. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 2631-2637, Austin Texas, USA, 2015. (Both authors contributed equally) (Matlab Code)
  • Lei Han, Yu Zhang, Guojie Song, and Kunqing Xie. Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 1854-1860, Quebec City, Quebec, Canada, 2014.
  • Yu Zhang. Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS), pp. 1896-1904, Lake Tahoe, Nevada, USA, 2013.
  • Yu Zhang and Dit-Yan Yeung. Learning High-Order Task Relationships in Multi-Task Learning. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1917-1923, Beijing, China, 2013.
  • Qing Bao, William K. Cheung, and Yu Zhang. Incorporating Structural Diversity of Neighbors in a Diffusion Model for Social Networks. In: Proceedings of the 2013 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 431-438, Atlanta, USA, 2013. (Best Student Paper Award)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Boosting by Exploiting Task Relationships. In: Proceedings of European Conference on Machine Learning and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 697-710, Bristol, UK, 2012. (Link)
  • Yu Zhang and Dit-Yan Yeung. Overlapping Community Detection via Bounded Nonnegative Matrix Tri-Factorization. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 606-614, Beijing, China, 2012. (Link)
  • Yu Zhang, Dit-Yan Yeung, and Eric P. Xing. Supervised Probabilistic Robust Embedding with Sparse Noise. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), pp. 1226-1232, Toronto, Ontario, Canada, 2012.
  • Yu Zhang and Dit-Yan Yeung. Discriminative Experimental Design. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 585-596, Athens, Greece, 2011. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Learning in Heterogeneous Feature Spaces. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI), pp. 574-579, San Francisco, California, USA, 2011.
  • Yu Zhang, Dit-Yan Yeung, and Qian Xu. Probabilistic Multi-Task Feature Selection. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS), pp. 2559-2567, Vancouver, Canada, 2010.
  • Yu Zhang and Dit-Yan Yeung. Worst-Case Linear Discriminant Analysis. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS), pp. 2568-2576, Vancouver, Canada, 2010.
  • Yu Zhang and Dit-Yan Yeung. A Convex Formulation for Learning Task Relationships in Multi-Task Learning. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 733-742, Catalina Island, California, 2010. (Best Paper Award) (Matlab Code)
  • Yu Zhang, Bin Cao, and Dit-Yan Yeung. Multi-Domain Collaborative Filtering. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 725-732, Catalina Island, California, 2010.
  • Yu Zhang and Dit-Yan Yeung. Transfer Metric Learning by Learning Task Relationships. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1199-1208, Washington, DC, USA, 2010. (Link)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Warped Gaussian Process for Personalized Age Estimation. In: Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2622-2629, San Francisco, CA, 2010. (Link) (Matlab Code)
  • Yu Zhang and Dit-Yan Yeung. Multi-Task Learning using Generalized t Process. In: Proceedings of the 13rd International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 964-971, Chia Laguna Resort, Sardinia, Italy, 2010.
  • Yan-Ming Zhang, Yu Zhang, Dit-Yan Yeung, Cheng-Lin Liu, and Xinwen Hou. Transductive Learning on Adaptive Graphs. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), pp. 661-666, Atlanta, Georgia, USA. 2010.
  • Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. Adaptive Transfer Learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), pp. 407-412, Atlanta, Georgia, USA, 2010.
  • Yu Zhang and Dit-Yan Yeung. Semi-Supervised Multi-Task Regression. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 617-631, Bled, Slovenia, 2009. (Link)
  • Yu Zhang and Dit-Yan Yeung. Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-Supervised Extension. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 602-616, Bled, Slovenia, 2009. (Link)
  • Yu Zhang and Dit-Yan Yeung. Semi-Supervised Discriminant Analysis via CCCP. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 644-659, Antwerp, Belgium, 2008. (Link)
  • Yu Zhang and Dit-Yan Yeung. Semi-Supervised Discriminant Analysis using Robust Path-based Similarity. In: Proceedings of the 21st IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, 2008. (Link)
  • Xin Geng, Zhi-Hua Zhou, Yu Zhang, Gang Li, and Honghua Dai. Learning from Facial Aging Patterns for Automatic Age Estimation. In: Proceeding of the 14th ACM International Conference on Multimedia (MM), pp. 307-316, Santa Barbara, CA, 2006.

    • Ph.D. Thesis

  • Yu Zhang. A Probabilistic Framework for Learning Task Relationships in Multi-Task Learning. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, August, 2011. (pdf)

    • Preprints

  • Zhixiong Yue, Feiyang Ye, Yu Zhang, Christy Liang, and Ivor W. Tsang. Deep Safe Multi-Task Learning. arXiv:2111.10601v2, 2021.
  • Shijie Chen, Yu Zhang, and Qiang Yang. Multi-Task Learning in Natural Language Processing: An Overview. arXiv:2109.09138, 2021.
  • Pengxin Guo, Yuancheng Xu, Baijiong Lin, and Yu Zhang. Multi-Task Adversarial Attack. arXiv:2011.09824, 2020.
  • Kaixiang Mo, Yu Zhang, Qiang Yang, and Pascale Fung. Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment. arXiv:1804.07691, 2018.
  • Guangneng Hu, Yu Zhang, and Qiang Yang. LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text. arXiv:1804.06201, 2018.
  • Kaixiang Mo, Yu Zhang, Qiang Yang, and Pascale Fung. Fine Grained Knowledge Transfer for Personalized Task-Oriented Dialogue Systems. arXiv:1711.04079, 2017.
  • Weiyan Wang, Yuxiang Wu, Yu Zhang, Zhongqi Lu, Kaixiang Mo, and Qiang Yang. Integrating User and Agent Models: A Deep Task-Oriented Dialogue System. arXiv:1711.03697, 2017.
  • Wenya Zhu, Kaixiang Mo, Yu Zhang, Zhangbin Zhu, Xuezheng Peng, and Qiang Yang. Flexible End-to-End Dialogue System for Knowledge Grounded Conversation. arXiv:1709.04264, 2017.

    • Book Chapters

  • Bin Cao, Yu Zhang, and Qiang Yang. Transfer Learning, Multi-Task Learning and Cost-Sensitive Learning. In Cost-Sensitive Machine Learning, B. Krishnapuram, S. Yu, and B. Rao (ed.), pp. 61-86, CRC Press, 2012. (Link)
  • Dit-Yan Yeung and Yu Zhang. Learning Inverse Dynamics by Gaussian Process Regression under the Multi-Task Learning Framework. In The Path to Autonomous Robots, G. S. Sukhatme(ed.), pp.131-142. Springer, 2009. (Link)

    • Workshop Papers

  • Yang Liu, Zhonglei Gu, Yu Zhang, and Yan Liu. Mining Emotional Features of Movies. In: Proceedings of the MediaEval 2016 Workshop, Hilversum, The Netherlands, 2016.
  • Yu Zhang. Age Estimation using Bayesian Process. In New Frontiers in Applied Data Mining, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Shenzhen, China, 2011.

    • Honors

  • Best Paper Award, National Science Review, 2020
  • Choice Outstanding Academic Title 2020, Choice Reviews
  • Best Paper Award, PAKDD Conference, 2019
  • Young National Distinguished Scholar, China, 2019
  • Distinguished Senior Program Committee Member, IJCAI Conference, 2018
  • Outstanding Reviewer, ICML Conference, 2018
  • Best Student Paper Award, WI Conference, 2013
  • Advanced Individual, Shenzhen Virtual University Park, 2013
  • SENG PhD Research Excellence Award, HKUST, 2011
  • Best Paper Award, UAI Conference, 2010
  • Overseas Research Award, HKUST, 2010-2011
  • Research Travel Award, NIPS, UAI, AISTATS, 2010
  • Champion, Postgraduate Student Research Paper Competition 2010, IEEE (Hong Kong) Computational Intelligence Chapter
  • Research Travel Award, HKUST, 2009
  • Second Runner-Up Award, Postgraduate Student Research Paper Competition 2009, IEEE (Hong Kong) Computational Intelligence Chapter
  • Postgraduate Scholarship, HKUST (2007-2011)
  • Best Paper Award, Jiangsu Province Computer Society, 2005
  • Excellent Undergraduate Scholarship, Nanjing University (2001-2004)

    • Professional Activities

    • Journal Reviewer

      Artificial Intelligence
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Journal of Machine Learning Research
      Machine Learning
      IEEE Transactions on Knowledge and Data Engineering
      IEEE Transactions on Multimedia
      IEEE Transactions on Neural Networks and Learning Systems
      IEEE Transactions on Systems, Man, and Cybernetics, Part B
      IEEE Transactions on Big Data
      IEEE Transactions on Aritificiall Intelligence
      ACM Transactions on Intelligent Systems and Technology
      Neural Computation
      National Science Review
      Statistical Analysis and Data Mining
      Pattern Recognition
      Physica A
      Knowledge and Information Systems
      Pattern Recognition Letter
      Information Sciences
      Neurocomputing
      Applied Intelligence

    • Conference (Senior) PC Member

      IJCAI (2013, 2015, 2016, 2017, 2018, 2019, 2020, 2021)
      AAAI (2016, 2017, 2018, 2019, 2020, 2021)
      UAI (2012, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021)
      NIPS/NeurIPS (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020)
      ICML (2014, 2016, 2018, 2021)
      AISTATS (2017, 2019, 2020, 2021)
      ICLR (2018, 2019, 2020, 2021)
      KDD (2015, 2016, 2017)
      ICPR (2014, 2016)
      ACML (2013, 2017, 2018, 2019, 2020, 2021)
      PRICAI (2014, 2016, 2018, 2019)
      EMNLP (2019, 2020)
      NAACL-HLT (2019, 2021)
      ICDM (2015)
      SDM (2013)
      CIKM (2017)
      CVPR (2022)

    Students

  • Lei Han (Exchange Student and Postdoctoral Associate, HKBU, 2013-2015)
  • Yi Zhang (Master Student, SUSTech, 2019)
  • Sicong Liang (Master Student, SUSTech, 2019)
  • Weisen Jiang (PhD Student, SUSTech, 2020)
  • Feiyang Ye (PhD Student, SUSTech, 2020)
  • Zhixiong Yue (Transferred PhD Student, SUSTech, 2020)
  • Ruihong Yang (Transferred Master Student, SUSTech, 2020)
  • Junchao Tian (Transferred Master Student, SUSTech, 2020)
  • Pengxin Guo (Master Student, SUSTech, 2020)
  • Shujun Yang (Postdoctoral, SUSTech, 2021)
  • Yanbin Wei (PhD Student, SUSTech, 2021)
  • Shaojie Zhang (PhD Student, SUSTech, 2021)
  • Qiushi Huang (Transferred PhD Student, SUSTech, 2021)
  • Fengpeng Yue (Transferred Master Student, SUSTech, 2021)
  • Qibing Bai (Transferred Master Student, SUSTech, 2021)
  • Jiabo Zhou (Master Student, SUSTech, 2021)
  • Yunhao Gou (PhD Student, SUSTech, 2022)
  • Zhan Zhuang (PhD Student, SUSTech, 2022)
  • Xuehao Wang (Master Student, SUSTech, 2022)
  • Shuhao Chen (Master Student, SUSTech, 2022)

    • Teaching Experience

  • CS102B: Introduction to Computer Programming B, Spring 2020, SUSTech
  • CSE5001: Advanced Artificial Intelligence, Fall 2019, 2020, SUSTech
  • MSBD 5012: Machine Learning, Spring 2018, HKUST
  • COMP 4331: Data Mining, Fall 2017, HKUST
  • MSBD 6000B: Deep Learning, Fall 2017, HKUST
  • COMP 7930: Big Data Analytics, Spring 2015, HKBU
  • COMP 7390: Algorithms for Financial Information Systems, Fall 2014, HKBU
  • COMP 3045 & 3790: Advanced Algorithm Design, Analysis and Implementation, Fall 2014, HKBU
  • COMP 7070: Advanced Topics in Machine Learning, Fall 2013, HKBU
  • COMP 7340: Enterprise Architecture and Integration, Spring 2013, HKBU
  • Short Course: Classic Data Mining Algorithms, Winter 2012, HKBU