Han-Jia Ye @ LAMDA, NJU

Han-Jia Ye  叶翰嘉
Han-Jia Ye (H.-J. YE)
Assistant Researcher
LAMDA Group
School of Artificial Intelligence
Nanjing University, Nanjing 210023, China.

Email: yehj [at] lamda.nju.edu.cn
               yehj [at] nju.edu.cn
               yhjyehanjia [at] gmail.com

Short Bio

Latest News

Main Research Interests

Learning with Similarity and Distance

Learning with Limited Data

Learning with Rich Semantics

Publications (Preprints)

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Publications (Conference Papers)

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  • Han-Jia Ye, Su Lu, De-Chuan Zhan. Distilling Cross-Task Knowledge via Relationship Matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, Washington, 2020. To appear. [Paper]

  • To reuse the cross-task knowledge, we distill the comparison ability and the local classification ability of the embedding and the top-layer classifier from a teacher model, respectively.

WSFG 
  • Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha. Learning Embedding Adaptation for Few-Shot Learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, Washington, 2020. To appear. [Paper][code]

  • For few-shot learning, we employ a type of self-attention mechanism to transform the embeddings from task-agnostic to task-specific in both seen and unseen classes.

WSFG 
  • Wei-Lun Chao*, Han-Jia Ye*, De-Chuan Zhan, Mark Campbell, Kilian Q. Weinberger. A Meta Understanding of Meta-Learning. In: The Adaptive and Multitask Learning (AMTL) 2019 Workshop, Long Beach, CA, 2019. [Paper] [ArXiv]

  • By rethinking meta-learning as a kind of supervised learning, we can borrow supervised learning tricks for the meta-learning paradigm.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou. Rectify Heterogeneous Models with Semantic Mapping. In: Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018. Page: 5630-5639. [Paper][code]

  • The reusability and evovability of a model are anlayzed in this paper. The proposed framework generates meta features and reuses model across heterogeneous feature domains.

WSFG 
  • Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan, Peng He. Distance Metric Facilitated Transportation between Heterogeneous Domains. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018. Page: 3012-3018. [Paper][code]

  • We deal with a specific problem for Optimal Transport Domain Adaptation. Our method extends the ability of OTDA to heterogeneous domains.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang. Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017. Page: 3315-3321. [Paper][code]

  • A robust Mahalanobis distance metric learning approach dealing with both instance and side-information uncertainty effectively.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Xiaolin Li, Zhen-Chuan Huang, Yuan Jiang. College Student Scholarships and Subsidies Granting: A Multi-Modal Multi-Label Approach. In: Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM'16), Barcelona, Spain, 2016, Page: 559–568. [Paper][code]

  • A multi-modal and multi-label method dealing with real-world college student scholarships and subsidies granting task.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou. What Makes Objects Similar: A Unified Multi-Metric Learning Approach. In: Advances in Neural Information Processing Systems 29 (NIPS'16), Barcelona, Spain, 2016, Page: 1235-1243. [Paper][code]

  • A unified multi-metric learning approach discovering various types of semantics under objects linkages. Besides, we provide a unified solver with theoretical guarantee.

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  • Han-Jia Ye, Xue-Min Si, De-Chuan Zhan, Yuan Jiang. Learning Feature Aware Metric. In: Proceedings of the 8th Asian Conference on Machine Learning (ACML'16), Hamilton, New Zealand, 2016, Page: 286–301. [Paper][code]

  • A fast decomposition strategy learning sparse/robust Mahalanobis distance metric.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Yuan Jiang. Instance Specific Metric Subspace Learning: A Bayesian Approach. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, Page: 2272-2278. [Paper]

  • A Bayesian perspective of distance metric learning, which can infer metric inductively.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Yuan Miao, Yuan Jiang, Zhi-Hua Zhou. Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM'15), Melbourne, Australia, 2015, Page: 991-1000. [Paper][code]

  • A novel rank consistency criterion is proposed for multi-view learning in a privacy-preserving scenario.

WSFG 
  • Yang Yang, Han-Jia Ye, De-Chuan Zhan, Yuan Jiang. Auxiliary Information Regularized Machine for Multiple Modality Feature Learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, Page: 1033-1039. [Paper]

  • Improve the prediction ability of cheap weak modal feature with the help of its strong counterpart.

Publications (Journal Papers)

WSFG 
  • Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan. Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. Machine Learning. 2020, Volume 109, pp 643–664. [Paper]

  • A practical meta-learning approach which efficiently adapts the task-specific initialization to an effective classifier.

WSFG 
  • Xiu-Shen Wei*, Han-Jia Ye*, Xin Mu, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou. Multi-instance learning with emerging novel class. IEEE Transactions on Knowledge and Data Engineering. To appear. [Paper]

  • A local metric learning approach to deal with the emerging novel class in multi-instance learning tasks.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Nan Li, Yuan Jiang. Learning Multiple Local Metrics: Global Consideration Helps. IEEE Transactions on Pattern Analysis and Machine Intelligence. To appear. [Paper]

  • By learning local metrics based on the global one, we try to adaptively allocate local metrics for heterogeneous data.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan. Few-Shot Learning via Model Composition (in Chinese). In: SCIENTIA SINICA Informatics (中国科学:信息科学). April 2020, Volume 50, Issue 5. [Paper]

  • We propose to compose classifiers inspired by the closed form of the least square loss, which fits learning with limited training examples.

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Yuan Jiang. Fast Generalization Rates for Distance Metric Learning. Machine Learning. February 2019, Volume 108, Issue 2, pp 267–295. [Paper]

  • Theoretical analysis of distance metric learning with fast generalization rate

WSFG 
  • Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou. What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. May 2019, Volume 41, Issue 5, pp 1257-1270. [Paper][Supplementary]

  • This manuscript extends our NIPS work. The concept of semantic metric, generalization analysis, and deep extension are introduced to get a more general framework.

  • 叶翰嘉, 詹德川. 度量学习研究进展. 中国人工智能学会通讯,2017,12:02-07. [Paper]
  • Xiaochuan Zou, Han-Jia Ye, De-Chuan Zhan. Image Classification and Concept Detection based on Strong and Weak Modality (in chinese with english abstract). Journal of Nanjing University, 2014,02:228-234.
  • Journal and Conference Reviewer

    TPAMI, TKDE, TKDD, TNNLS, Neurocomputing, AAAI 2021, IJCAI 2021, ECML/PKDD 2021, NeurIPS 2020, ICDM 2020, CVPR 2020, IJCAI 2020, NeurIPS 2019, CVPR 2019, ICCV 2019, IJCAI 2019, AAAI 2019, ACML 2019, ICLR 2019, NeurIPS 2018, ACML 2018, AAAI 2018, IJCAI 2018, CIKM 2017, IJCAI 2017, KDD 2017, PAKDD 2017, SDM 2017, AISTATS 2017, AAAI 2017, NIPS 2016, IJCAI 2016, ICPR 2016, AAAI 2015, IJCAI 2015, PAKDD 2015

    Course

    Correspondence

    Office: Room A205, Yifu Building, Xianlin Campus of Nanjing University
    Address: Han-Jia Ye
                     National Key Laboratory for Novel Software Technology
                     Nanjing University, Xianlin Campus Mailbox 603
                     163 Xianlin Avenue, Qixia District, Nanjing 210046, China