Biography: Hi, I am Associate Professor with the College of Sciences, National University of Defense Technology (NUDT), Changsha, Hunan, China. He has received his B.Eng. and Ph.D degree (advised by Prof. Dongyun Yi) from NUDT. He was a visiting Ph.D. student (advised by Prof. Jieping Ye) with the University of Michigan from 2015 to 2017. He has authored more than 50 papers in journals and conferences, such as IEEE TPAMI, IEEE TKDE, TCYB, Pattern Recognition and KDD etc. His research interests focus on machine learning, multi-media analysis, data mining and scene understanding. Dr. Luo received Outstanding Doctoral Dissertation Award of SESC and Hunan Province in 2020. He has served as AC/SPC/PC of several conferences including NeurIPS, ICLR, ICML, IJCAI, AAAI and ICPR etc.

Email: tingjinluo@hotmail.com
Follow me: DBLP   Researchgate   GitHub

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News and Events

  • 2024.08.11: One papers “Imbalanced Multi-instance Multi-label Learning via Tensor Product-based Semantic Fusion” accepted by Frontiers of Computer Science. Congratulations to Xinyue Zhang and all coauthors.

  • 2024.07.21: Two papers accepted by ACM MM. Congratulations to Quangjiang Li, Xinyue Zhang and all coauthors.

  • 2023.12.19: One paper “Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation” accepted by Frontiers of Computer Science. Congratulations to Yueyin Liu and all coauthors.

  • 2023.12.09：One paper “Deep Incomplete Multi-View Learning Network with Insufficient Label Information” accepted by 38th AAAI Conference on Artificial Intelligence (AAAI-24). Congratulations to Zhangqi Jiang and all coauthors.

  • 2023.06.21：One paper “Incomplete Multi-view Learning under Label Shift” accepted by IEEE Transactions on Image Processing. Congratulations to Ruidong Fan and all coauthors.

  • 2023.01.27：One paper “Absent Multi-view Semi-supervised Classification” accepted by IEEE Transactions on Cybernetics. Congratulations to Wenzhang Zhuge and all coauthors.

  • 2022.08.09：One paper “Debiased Scene Graph Generation for Dual Imbalance Learning” accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. Congratulations to Hao Zhou and all coauthors.

  • 2022.05.27: One paper “Active Label Distribution Learning via Kernel Maximum Mean Discrepancy” accepted by Frontiers of Computer Science. Congratulations to Xinyue Dong and all coauthors.

  • 2021.12.29: One paper “Safe Incomplete Label Distribution Learning” accepted by Pattern Recognition. Congratulations to Jing Zhang and all coauthors.

  • 2021.10.06: One paper “A Unified Deep Sparse Graph Attention Network for Scene Graph Generation” accepted by Pattern Recognition. Congratulations to Hao Zhou and all coauthors.

  • 2021.04.27: One paper “Semi-supervised Learning with Label Proportion” accepted by IEEE TKDE. Congratulations to Ningzhao Sun and all coauthors.

  • 2021.03.06: One paper “Relationship-aware Primal-Dual Graph Attention Network for Scene Graph Generation” accepted by ICME 2021. Congratulations to Hao Zhou and all coauthors.

  • 2021.02.09: One paper “Multiple Instance Learning for Unilateral Data” accepted by PAKDD 2021. Congratulations to Xijia Tang and all coauthors.

Opening Positions

  • There are opening research positions in my group at all levels, including Postdoc, research assistant, Ph.D. student, and master student. Please send me an E-mail which contains your CV, score sheet, certificates, and representative publications if you are interested.

  • I am always looking for self-motivated undergraduate students (mainly @NUDT) to join my group for research training!

Prospective students

I am currently recruiting self-motivated PhD and MPhil students to join our group. Please carefully read the following contents before sending me an E-mail:

Research interests: I mainly study how to design advanced machine learning algorithms using mathematical tools, which can be further applied to computer vision, data mining, and other fields. Recent research interests include weakly supervised learning (e.g., semi-supervised learning, label noise robust learning, PU learning, partial label learning, etc.), interactive machine learning (curriculum learning, machine teaching, etc.).

Admission expectations: I prefer the students who have solid mathematical foundations, strong programming skills, and good English abilities (listening/speaking/reading/writing). The experiences in research (e.g. academic competition, publications, extracurricular academic activities, etc.) will be a benefit. Besides, I also recommend you take Machine Learning Course instructed by Hung-yi Lee (or at least one of pattern recognition or computer vision classes) before getting into machine learning research.

Training objectives: 1) After carefully organized scientific research training, the students are expected to gain solid professional knowledge, rigorous scientific thinking, strong practical ability, broad academic vision, and important scientific literacy for independent problem discovery, thinking, and solving; 2)Publish high-level and impactful papers in top journals or conferences, and is interested in continuing academic research after graduation.

My promise: 1) Since I have always been in the frontline of scientific research, I will provide you a full range of effective guidance in algorithm design, coding, experimental verification, paper writing, patent application, presentation, etc. 2) Provide you enjoyable scientific research atmosphere, comfortable research environment, generous allowance/rewards, and efficient computing equipment; 3) I will not let you deal with administrative affairs that are not related to your research, so as to ensure that you have sufficient time for your research; 4) For the students who succesfully get their papers accepted by top conferences, I will try my best to financially support them to participate in the conferences; 5) For outstanding PhD students, I will provide a joint training opportunity with overseas universities with a duration of 0.5-2 years; 6) For outstanding graduates, I would be happy to write recommendation letters for job hunting or university application.

Machine Learning Resources

Books and Chapters

Preprints and Working Papers

(* indicates equal contributions and # indicates advisees)

  • Hao Zhou, Jun Zhang, Tingjin Luo#, Jun Lei, Shuohao Li, Not the Statistical Bias, Mining the Essential Causality for Scene Graph Generation via Causal Association Extraction Network, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, Under Review. (JCR Q1, CCF A, IF: 24.314) [PDF]

  • Ruidong Fan, Xiao Ouyang, Tingjin Luo, Chenping Hou. Label Shift Correction via Confidence-Guided Self-Training. NeurIPS, 2023, Review. (CCF A) [PDF]

  • Hao Zhou, Tingjin Luo#, Yongming He, Jun Zhang. Dynamic Multi-experts Collaboration Learning for Long-tailed Visual Recognition, The 33rd International Joint Conference on Artificial Intelligence, 2023, Under Review. (CCF A)

  • Pinhan Fu, Xinyan Liang, Tingjin Luo, Yuhua Qian. Core-Structures-Guided Multi-Modal Classification Neural Architecture Search. The 33rd International Joint Conference on Artificial Intelligence, 2024, Under Review. (CCF A)

  • Feijiang Li, Yuhua Qian, Jieting Wang, Hongren Yan, Tingjin Luo. SoCo: Second Order Induced Co-association Relation for Clustering Ensemble, 2024, Under Review.

Publications

(* indicates equal contributions, and # indicates advisees)

2024

  • Zhangqi Jiang, Tingjin Luo#, Xinyan Liang. Deep Incomplete Multi-View Learning Network with Insufficient Label Information, 38th AAAI Conference on Artificial Intelligence (AAAI-24), Accepted. (CCF A) [PDF]

  • Yueying Liu, Tingjin Luo#. Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation. Frontiers of Computer Science, 2024, Accepted. (JCR Q2, CCF B) [PDF]

  • Hong Tao, Jiacheng Jiang, Chenping Hou#, Tingjin Luo, Ruidong Fan, Jing Zhang, Compound Weakly Supervised Clustering, IEEE Transactions on Image Processing, 2024, 33(1): 957 - 971. (JCR Q1, CCF A, IF: 11.041) [PDF]

2023

  • Hao Zhou, Jun Zhang#, Tingjin Luo#, Yazhou Yang, Jun Lei, Debiased Scene Graph Generation for Dual Imbalance Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4274-4288, 1 April 2023. (JCR Q1, CCF A, IF: 24.314) [PDF]

  • Wenzhang Zhuge, Tingjin Luo#, Hong Tao, Chenping Hou#, Dongyun Yi, Absent Multi-view Semi-supervised Classification, IEEE Transactions on Cybernetics, 2023, Accepted. (JCR Q1, IF: 19.118) [PDF]

  • Xinyue Dong, Tingjin Luo#, Ruidong Fan, Wenzhang Zhuge, Chenping Hou#, Active Label Distribution Learning via Kernel Maximum Mean Discrepancy, Frontiers of Computer Science, 2023, 17(4): 174327. (JCR Q2, CCF B, CCF T1, IF: 2.669) [PDF]

  • Ningzhao Sun*, Tingjin Luo*, Hong Tao, Chenping Hou#, Dewen Hu#, Semi-supervised Learning with Label Proportion, IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 877-890, 1 Jan. 2023. (CCF A, IF: 9.235) [PDF]

  • Yueying Liu, Tingjin Luo#. Joint Distribution Alignment and Transfer Subspace Learning for Unsupervised Domain Adaptation. IEEE 9th International Conference on Cloud Computing and Intelligence Systems, Accepted, 2023.

  • Shilin Gu, Tingjin Luo, Ming He, and Chenping Hou, Online Learning with Incremental Feature Space and Bandit Feedback, IEEE Transactions on Knowledge and Data Engineering, 2023, Accepted. (CCF A, IF: 9.235) [PDF]

  • Ruidong Fan, Xiao Ouyang, Tingjin Luo, Dewen Hu, Chenping Hou. Incomplete Multi-view Learning under Label Shift. IEEE Transactions on Image Processing, 2023.06, Accepted. (JCR Q1, CCF A, IF: 11.041) [PDF]

  • Xiuqi Huang, Haotian Ni, Tingjin Luo, Hong Tao, Chenping Hou#, Decorrelated Spectral Regression: An Unsupervised Dimension Reduction Method under Data Selection Bias, Neurocomputing, Accepted, 2023. (JCR Q2, IF: 5.779) [PDF]

2022

  • Jing Zhang, Hong Tao#, Tingjin Luo, Chenping Hou#, Safe Incomplete Label Distribution Learning, Pattern Recognition, Volume 125, May 2022, 108518. (JCR Q1, CCF B, IF: 8.518) [PDF]

  • Wenzhang Zhuge, Hong Tao, Tingjin Luo#, Linli Zeng, Chenping Hou#, Dongyun Yi, Joint Representation Learning and Clustering: A Framework for Grouping Partial Multiview Data, IEEE Transactions on Knowledge and Data Engineering, 34(8): 3826 - 3840, 2022.08.01, DOI: 10.1109/TKDE.2020.3028422. (CCF A, IF: 9.235) [PDF]

  • Xijia Tang, Chao Xu, Tingjin Luo, Chenping Hou#. Multi-instance positive and unlabeled learning with bi-level embedding, Intelligent Data Analysis, 26(3): 659-678, 2022.

2021

  • Hao Zhou, Yazhou Yang, Tingjin Luo#, Jun Zhang#, Jun Lei, Shuohao Li, A Unified Deep Sparse Graph Attention Network for Scene Graph Generation, Pattern Recognition, 2021, Accepted. (JCR Q1, CCF B, IF: 8.518) [PDF]

  • Hao Zhou, Tingjin Luo#, Jun Zhang, Jun Lei, Shuohao Li, Relationship-aware Primal-Dual Graph Attention Network for Scene Graph Generation, in IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China, July 5-9, 2021, Accepted. (CCF B) [PDF]

  • Xijia Tang, Tingjin Luo#, Tianxiang Luan, Chenping Hou, Multiple Instance Learning for Unilateral Data, in Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Accepted, Delhi, India, 2021. (CCF C) [PDF]

  • Xinyue Dong, Shilin Gu, Wenzhang Zhuge, Tingjin Luo, Chenping Hou#, Active Label Distribution Learning, Neurocomputing, vol. 436, pp. 12-21, 2021, DOI: 10.1016/j.neucom.2020.12.128. (JCR Q1, CCF B, IF: 5.779) [PDF]

2020

  • Ruidong Fan, Tingjin Luo, Wenzhang Zhuge, Sheng Qiang, Chenping Hou#, Multi-view Subspace Learning via Bidirectional Sparsity, Pattern Recognition, 108: 107524, 2020, DOI: 10.1016/j.patcog.2020.107524, (JCR Q1, CCF B, IF: 8.518). [PDF][Code]

  • Tianxiang Luan, Tingjin Luo, Wenzhang Zhuge, Chenping Hou#, Optimal Representative Distribution Margin Machine for Multi-Instance Learning, IEEE Access, vol. 8, pp. 74864-74874, 2020, DOI: 10.1109/ACCESS.2020.2988764. (IF: 3.745). [PDF]

  • Wenzhang Zhuge, Tingjin Luo, Hong Tao, Chenping Hou#, Dongyun Yi, Multi-View Spectral Clustering With Incomplete Graphs, IEEE Access, vol. 8, pp. 99820-99831, 2020, DOI: 10.1109/ACCESS.2020.2997755. (IF: 3.745) [PDF]

2019

  • Tingjin Luo, Chenping Hou#, Feiping Nie, Dongyun Yi, Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis, IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 933-946, March 2019, DOI: 10.1109/TCYB.2018.2789524. (JCR Q1, IF: 19.118) [PDF][CODE]

  • Zhipeng Lin, Zhenyu Zhao, Tingjin Luo#, Wenjing Yang, Yongjun Zhang, Yuhua Tang, Non-Convex Transfer Subspace Learning for Unsupervised Domain Adaptation, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2019), Shanghai, China, 2019, pp. 1468-1473, DOI: 10.1109/ICME.2019.00254, EI: 20193407349323. (CCF B) [PDF]

  • Haotian Wang, Wenjing Yang#, Zhenyu Zhao, Tingjin Luo, Ji Wang, Yuhua Tang, Rademacher Dropout: An Adaptive Dropout for Deep Neural Network via Optimizing Generalization Gap, Neurocomputing, vol. 357, pp. 177-187, 2019, DOI: 10.1016/j.neucom.2019.05.008. (JCR Q1, CCF B, IF: 5.779) [PDF]

  • Haodong Yang, Jun Zhang#, Shuohao Li#, Tingjin Luo, Bi-direction Hierarchical LSTM with Spatial-Temporal Attention for Action Recognition, Journal of Intelligent & Fuzzy Systems, vol.36, no. 1, pp. 775-786, 2019, DOI: 10.3233/JIFS-18209. (IF: 1.851) [PDF]

2018

  • Tingjin Luo, Chenping Hou#, Feiping Nie, Hong Tao, Dongyun Yi, Semi-Supervised Feature Selection via Insensitive Sparse Regression with Application to Video Semantic Recognition, IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 10, pp. 1943-1956, 2018, DOI: 10.1109/TKDE.2018.2810286, WOS:000444603900009. (CCF A, IF: 9.235) [PDF]

  • Weizhong Zhang*, Tingjin Luo*, Shuang Qiu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang#, Identifying Genetic Risk Factors for Alzheimer’s Disease via Shared Tree-Guided Feature Learning Across Multiple Tasks, IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, pp. 2145-2156, 2018, DOI: 10.1109/TKDE.2018.2816029, WOS:000446795900010. (CCF A, IF: 9.235) [PDF]

  • Gongmin Lan, Chenping Hou#, Feiping Nie, Tingjin Luo, Dongyun Yi, Robust Feature Selection via Simultaneous Capped Norm and Sparse Regularizer Minimization, Neurocomputing, vol. 283, pp. 228-240, 2018, DOI: 10.1016/j.neucom.2017.12.055, WOS:000424896600021. (JCR Q1, CCF B, IF: 5.779) [PDF]

2017

  • Tingjin Luo, Weizhong Zhang, Shuang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye#, Jie Wang#, Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2017), Halifax, Canada, August 2017, pp. 345-354, DOI: 10.1145/3097983.3097984, EI: 20173704157018. (CCF A) [PDF]

  • Yang Yang*, Tingjin Luo*, Zhoujun Li, Xiaoming Zhang, Philip S. Yu, A Robust Method for Inferring Network Structures, Scientific Reports, vol. 7, no. 5221, pp. 1-12, 2017, DOI: 10.1038/s41598-017-04725-2, WOS:000405425000025. (JCR Q2, IF: 4.576)[PDF]

  • Chenping Hou, Yuanyuan Jiao, Feiping Nie, Tingjin Luo, Zhi-Hua Zhou#, 2D Feature Selection by Sparse Matrix Regression. IEEE Transations on Image Processing, vol. 26, no. 9, pp. 4255-4268, 2017, DOI: 10.1109/TIP.2017.2713948, WOS:000405395900001. (CCF A, IF: 9.340) [PDF]

  • Guangtao Wang, Jiayu Zhou, Jingjie Ni, Tingjin Luo, Wei Long, Hai Zhen, Gao Cong, Jieping Ye#, Robust Self-Tuning Sparse Subspace Clustering, Procedings of 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017), pp.858-865, New Orleans, USA, 2017, DOI: 10.1109/ICDMW.2017.117, WOS:000425845700114. [PDF]

Before 2017

  • Tingjin Luo, Chenping Hou#, Dongyun Yi, Jun Zhang, Discriminative Orthogonal Elastic Preserving Projections for Classification, Neurocomputing, vol. 179, pp. 54-68, 2016, DOI: 10.1016/j.neucom.2015.11.037, WOS:000370090300005. (JCR Q1, CCF B, IF: 5.779) [PDF]

  • Tingjin Luo, Jun Zhang, Lin Lian, Speed up Junction Detector based on Azimuth Consensus by Harris Corner, Optical Review, vol. 21, no. 2, pp. 135-142, Mar. 2014, DOI: 10.1007/s10043-014-0021-1, WOS:000334253000005. (IF: )

  • Jun Zhang, Tingjin Luo#, Gui Gao, Lin Lian, Junction Point Detection Algorithm for SAR Image, International Journal of Antennas and Propagation, vol. 2013, pp. 1-9, Mar. 2013, DOI: 10.1155/2013/357379, WOS:000317567100001. (IF: 1.207) [PDF]

  • Jun Zhang, Weiqiang Huang, Tingjin Luo, 3D Reconstruction of Periodic Human Walking Trajectories Based on Single View, Chinese Journal of Electronics, vol. 22, no. 3, pp.455-460, July 2013, WOS:000321481000004. (JCR Q4, IF: 0.941)

  • Jun Zhang, Shukui Xu, Kuihua Huang, Tingjin Luo, Accurate Moving Target Detection Based on Background Subtraction and SUSAN, International Journal of Computer and Electrical Engineering, vol. 4, no. 4, pp.436-439, Aug. 2012, DOI: 10.7763/IJCEE.2012.V4.529, WOS:000321481000004. [PDF]

Group Members

PhD Students

  • Yueying Liu (2024-)

Master Students

  • Yueying Liu (2021-2023)

  • Xingyue Zhang (2022-)

  • Quanjiang Li (2023-)

  • Zhangqi Jiang (2023-)

  • Huiting Yuan (2023-)

Education & Work

  • 06/2020-Now: National University of Defense Technology (NUDT), Associate Professor in College of Sciences

  • 12/2020-06/2022: National University of Defense Technology (NUDT), Associate Professor in College of Liberal Arts and Sciences

  • 06/2018-12/2020: National University of Defense Technology (NUDT), Assistant Professor in College of Liberal Arts and Sciences

Awards and Honors

  • Outstanding Talents Program of NUDT, 07/2021
  • Fund for NUDT Young Innovator Awards, 12/2020
  • Outstanding Doctoral Dissertation Award of SESC, 10/2020 (4 in China)
  • Outstanding Doctoral Dissertation Award of Hunan Province, 08/2020
  • NUDT Postgraduate Academic Innovation Star, 10/2017 (10 out of 300+)
  • NUDT ZhouMingXi Scholarship, 12/2017
  • CSC Scholarship, 10/2015-09/2017

Teaching

  • Probability and Mathematical Statistics – 2020 Spring, 2021 Spring, NUDT
  • Statistical Forecasting and Decision Making – 2019 Fall, 2020 Fall, 2021 Fall, NUDT
  • Multivariate Statistical Analysis – 2020 Fall, NUDT

Projects & Grants

  • “Robust Multiple Instance Learning via Sparse Optimization”, National Science Foundation of China, 2020–2022, Principal Investigator.

Talks

  • 08/2017: Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Halifax, Canada (Oral)

Service

  • Area Chair: Dynamic Feature Mining of ICDM Workshop

  • Conference Program Committee: AAAI 2019-2020, ICLR 2019-2021, NeurIPS 2019-2020, IJCAI 2018-2022, ICPR 2016-2021

  • Journal Reviewer: IEEE TKDE, IEEE Access, 中国科学, Neurocomputing, Plos One, Information Processing & Management, International Journal of Intelligent Systems

Alumni

Contact

Address: College of Liberal Arts and Sciences, National University of Defense Technology: No.1 Fuyuan Road, Kaifu District, Changsha, Hunan, China, 410073

Office: Room 4201A, College of Liberal Arts and Sciences, National University of Defense Technology