Publications

  • Journal Papers

PUBLICATIONS

MTL Survey

Book

  • Qiang Yang, Yu Zhang, Wenyuan Dai, and Sinno Jialin Pan. Transfer Learning. Cambridge University Press, 2020.

Refereed Journal Papers

Refereed Conference Papers

  • 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), Nice, France, 2019.
  • 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 (formerly known as 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), 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 IEEE 2nd 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), 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.
  • 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) (Link)
  • 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. (Link)
  • 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 ZhangMulti-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 ZhangReduction 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. (Link)
  • 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) (Link) (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. (Link)
  • Yu ZhangMulti-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 ZhangLearning 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 ZhangDiscriminative 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 ZhangHeterogeneous-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 ZhangIncorporating 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. (Link)

Ph.D. Thesis

  • Yu ZhangA 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)

Technical Reports

Book Chapters

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 MiningPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Shenzhen, China, 2011. (Link)

Papers in Chinese

  • Yu Zhang and Zhi-Hua Zhou. A New Age Estimation Method based on Ensemble LearningACTA AUTOMATICA SINICA, 34(8): 997-1000, 2008.
  • Yu Zhang and Zhi-Hua Zhou. Research on Facial Features that Impact Gender Classification. Journal of Jiangsu Polytechnic University, 17: 9–12, 2005. (Best Paper Award by Jiangsu Province Computer Society 2005)

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