Sheng Li

Assistant Professor, Department of Computer Science 
Institute for Artificial Intelligence (Affiliated) 
Institute of Bioinformatics (Affiliated) 

804 Boyd GSRC, University of Georgia, Athens, GA 30602
Email: sheng.li [AT] uga.edu

Research Interests: Trustworthy Representation Learning (e.g., Robustness, Fairness, Causality, Transferability); Visual Intelligence; User Modeling; Natural Language Understanding; Bioinformatics; Biomedical Informatics.

Education: Ph.D. in CE, Northeastern, 2017; M.S. in CS, NUPT, 2012; B.S. in CS, NUPT, 2010.

Openings: I am continuously looking for highly-motivated Ph.D. students to work on machine learning, computer vision and causal inference. Please send me your CV if interested.

News [more...]

  • 12/2021, I receive the Fred C. Davidson Early Career Scholar Award.
  • 12/2021, Three papers on causal inference, unsupervised domain adaptation, and multi-modal learning are accepted at SDM 2022.
  • 10/2021, One paper on cross-domain clustering is accepted to the student abstract and poster program at AAAI 2022.
  • 10/2021, One paper on knowledge-derived query suggestion is accepted at IEEE BigData 2021.
  • 10/2021, One paper on unsupervised domain adaptation is accepted by IEEE Trans. Neural Networks and Learning Systems (T-NNLS).
  • 08/2021, One paper on interpretable deep learning for bioinformatics is accepted by Nature Communications.
  • 08/2021, Two papers on universal domain adaptation and physics-guided machine learning are accepted at IEEE ICDM 2021.
  • 08/2021, I gave a keynote talk at the 3rd Workshop on Continual and Multimodal Learning for Internet of Things (Co-located with IJCAI 2021).
  • 08/2021, One paper on causal inference is accepted at ACM CIKM 2021.
  • 07/2021, I accepted the invitations to serve as Area Chair of ICLR 2022 and Senior Program Committee member of AAAI 2022.
  • 06/2021, One paper on offensive language detection with BERT is accepted at ACL-WOAH 2021.
  • 05/2021, One paper on knowledge-guided article embedding is accepted by IEEE Trans. Neural Networks and Learning Systems (T-NNLS).
  • 05/2021, One paper on causal inference from networked observational data is accepted at ACM SIGKDD 2021.
  • 05/2021, My PhD students Saed Rezayi and Ronghang Zhu received the Excellent Graduate Students Research Awards from CS department.
  • 05/2021, I received the Faculty Teaching Excellence Award from the Department of Computer Science.
  • 04/2021, One paper on pose guided human image generation is accepted at ACM ICMR 2021.
  • 04/2021, One paper on self-tuning graph neural networks is accepted at ACM SIGIR 2021.
  • 03/2021, I will co-organize The 10th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at CVPR 2021.
  • 03/2021, Three papers are accepted at NAACL 2021, IEEE ICME 2021 and IEEE MIPR 2021.
  • 02/2021, Received a Cisco Gift Grant (Sole PI) to support our research on fair machine learning.
  • 02/2021, Received an ARO Grant (Sole PI) to support our research on graph reasoning and visual understanding.
  • 12/2020, Our survey paper on causal inference is accepted by ACM Transactions on Knowledge Discovery from Data (TKDD).
  • 12/2020, I accepted the invitation to serve as Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT).
  • 12/2020, One paper on multi-view time series classification is accepted at AAAI 2021.
  • 12/2020, Received a DoD Grant (Sole PI) to support our research on representation learning and visual understanding.

Awards and Honors

  • Fred C. Davidson Early Career Scholar Award, 2022
  • CS Faculty Teaching Excellence Award, 2021
  • INNS Aharon Katzir Young Investigator Award, 2020
  • CS Faculty Research Excellence Award, 2020
  • M. G. Michael Award, 2020
  • Adobe Data Science Research Award, 2019
  • Baidu Research Fellowship, 2016-2017
  • Chinese Government Award for Outstanding Self-Financed Students Abroad, 2015-2016
  • The inaugural Adobe Research Fellowship Finalist, 2015
  • NEU Outstanding Graduate Student Award, 2014-2015
  • Best Paper Award, SDM 2014
  • Best Paper Award Candidate, ICME 2014
  • Best Student Paper Honorable Mention Award, FG 2013
  • Student Travel Awards for SDM 2014 / ICDM 2014 / CIKM 2015 / CVPR 2016 / CIKM 2016 / IJCAI 2016 / KDD 2016

Teaching

  • Spring 2022, CSCI 3360: Data Science I; STAT/CSCI 4990: Data Science Capstone Course
  • Fall 2021, CSCI 8945: Advanced Representation Learning
  • Spring 2021, CSCI 3360: Data Science I
  • Fall 2020, CSCI 8945: Advanced Representation Learning
  • Spring 2020, CSCI 8950: Machine Learning
  • Fall 2019, CSCI 8945: Advanced Representation Learning
  • Spring 2019, CSCI 3360: Data Science I
  • Fall 2018, CSCI 8000: Advanced Topics in Machine Learning

Services

  • Panelist: NSF, CDC
  • Associate Editor: IEEE Transactions on Circuits and Systems for Video Technology (2021 - ), IEEE Computational Intelligence Magazine (2019 - ), Neurocomputing (2017 - ), Journal of Electronic Imaging (2018 - ), IET Image Processing (2017 - 2020)
  • Editorial Board Member: Frontiers in Signal Processing (2021 - ), Neural Computing and Applications (2017 - )
  • Program Chair: CVPR-AMFG (2021), IJCAI-Tusion (2020), IJCAI-Tusion (2019), CVPR-AMFG (2019)
  • Publicity Chair: ICMLA (2016), TCMFTL (2016), AMFG (2015-2017)
  • Area Chair: ICLR (2022), ICPR (2020), VCIP (2017)
  • SPC Member: IJCAI (2020-2021), AAAI (2019-2022)
  • PC Member: NeurIPS (2019-2020), ICML (2019-2021), CVPR (2019-2022), ACL (2021-2022), SDM (2022), NAACL (2021), ECCV (2020), EMNLP (2020), ECAI (2020), ICCV (2019-2021), IJCAI (2015-2019), KDD (2018-2021), ICLR (2019-2021), UAI (2019-2020), IEEE BigData(2019-2020), BMVC (2019), NIPS (2018), AAAI (2017, 2018), ACM MM (2018), MIPR (2018-2020), PAKDD (2017-2020), IEEE BigData (2018-2020), ICTAI (2018), AFFCON (2018-2020), DSAA (2017), FG (2017), ACII (2017), NLPCC (2017)
  • Reviewer: IEEE TPAMI / TKDE / TIP / TNNLS / TMM / TBD / TC / TCSVT / TETCI / TMC, ACM CSUR / TKDD / TOSN / TIST / TOMM, Pattern Recognition, Neurocomputing, PLoS ONE, IJPRAI, JVCI, JEI, OE, etc.

Selected Publications [Full List] [Google Scholar]

Journal Papers

  1. Rahil Taujale*, Zhongliang Zhou*, Wayland Yeung, Kelley Moremen, Sheng Li, and Natarajan Kannan. Mapping the glycosyltransferase fold landscape using interpretable deep learning. Nature Communications, 2021. (* indicates equal contribution)
  2. Heng-Shiou Sheu, Zhixuan Chu, Daiqing Qi, and Sheng Li. Knowledge-Guided Article Embedding Refinement for Session-based News Recommendation. IEEE Trans. Neural Networks and Learning Systems (TNNLS), 2021.
  3. Ronghang Zhu, Xiaodong Jiang, Jiasen Lu, and Sheng Li. Cross-Domain Graph Convolutions for Adversarial Unsupervised Domain Adaptation. IEEE Trans. Neural Networks and Learning Systems (TNNLS), 2021.
  4. Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. A Survey on Causal Inference. ACM Trans. Knowledge Discovery from Data (TKDD), 2021.
  5. Xiaodong Jiang, Ronghang Zhu, Pengsheng Ji, and Sheng Li. Co-embedding of Nodes and Edges with Graph Neural Networks. IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), 2020. [Code]
  6. Rahil Taujale, Aarya Venkat, Liang-Chin Huang, Zhongliang Zhou, Wayland Yeung, Khaled M Rasheed, Sheng Li, Arthur S Edison, Kelley W Moremen, Natarajan Kannan. Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases. eLife, 2020.
  7. Jiahuan Ren, Zhao Zhang, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, and Meng Wang. Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space. IEEE Trans. Image Processing (T-IP), 2020.
  8. Liang‑Chin Huang, Wayland Yeung, Ye Wang, Huimin Cheng, Aarya Venkat, Sheng Li, Ping Ma, Khaled Rasheed, and Natarajan Kannan. Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction. BMC Bioinformatics, 2020.
  9. Sheng Li*, Zhiqiang Tao*, Kang Li, Yun Fu. Visual to Text: Survey of Image and Video Captioning. IEEE Trans. Emerging Topics in Computational Intelligence (T-ETCI), 2019. (* indicates equal contribution)
  10. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. Marginalized Multi-View Ensemble Clustering. IEEE Trans. Neural Networks and Learning Systems (TNNLS), 2019.
  11. Zhao Zhang, Yan Zhang, Sheng Li, Guangcan Liu, Shuicheng Yan, and Meng Wang. Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering. IEEE Trans. Knowledge and Data Engineering (T-KDE), 2019.
  12. Zhengming Li, Zheng Zhang, Jie Qin, Sheng Li, and Hongmin Cai. Low-Rank Analysis–Synthesis Dictionary Learning with Adaptively Ordinal Locality. Neural Networks (NN), 2019.
  13. Sheng Li, Ming Shao, and Yun Fu. Person Re-identification by Cross-View Multi-Level Dictionary Learning. IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), 2018. [Code]
  14. Sheng Li, Kang Li, and Yun Fu. Self-Taught Low-Rank Coding for Visual Learning. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 29(3): 645-656, 2018. [Code]
  15. Sheng Li, Kang Li, and Yun Fu. Early Recognition of 3D Human Actions. ACM Trans. Multimedia Computing Communications and Applications (TOMM), 14(1s): 20:1-20:21, 2018.
  16. Sheng Li, Ming Shao, and Yun Fu. Multi-View Low-Rank Analysis with Applications to Outlier Detection. ACM Trans. Knowledge Discovery from Data (TKDD) , 12(3): 32:1-32:22, 2018. [Code]
  17. Hongfu Liu, Ming Shao, Sheng Li, and Yun Fu. Infinite Ensemble Clustering. Data Mining and Knowledge Discovery (DMKD), 32(2): 385-416, 2018.
  18. Chengcheng Jia, Ming Shao, Sheng Li, Handong Zhao, Yun Fu. Stacked Denoising Tensor Auto-Encoder for Action Recognition with Spatiotemporal Corruptions, IEEE Trans. Image Processing (T-IP), 27(4): 1878-1887, 2018.
  19. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, Yun Fu: Robust Spectral Ensemble Clustering via Rank Minimization. ACM Trans. Knowledge Discovery from Data (TKDD) , 2018.
  20. Yan Zhang, Zhao Zhang, Sheng Li, Jie Qin, Guangcan Liu, Meng Wang, Shuicheng Yan: Unsupervised Nonnegative Adaptive Feature Extraction for Data Representation. IEEE Trans. Knowledge and Data Engineering (T-KDE), 2018.
  21. Kai Li, Zhengming Ding, Sheng Li, Yun Fu: Towards Resolution-Invariant Person Re-identification via Projective Dictionary Learning. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 2018.
  22. Kang Li, Sheng Li, Sangmin Oh, Yun Fu. Videography based Unconstrained Video Analysis, IEEE Trans. Image Processing (T-IP), 26(5): 2261-2273, 2017.
  23. Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: SLMOML: Online Metric Learning with Global Convergence, IEEE Trans. Circuits and Systems for Video Technology (T-CSVT), 2017.
  24. Sheng Li, Yun Fu: Learning Robust and Discriminative Subspace with Low-Rank Constraints. IEEE Trans. Neural Networks and Learning Systems (T-NNLS), 27(11): 2160-2173, 2016. [Code]
  25. Sheng Li, Yun Fu: Learning Balanced and Unbalanced Graphs via Low-Rank Coding. IEEE Trans. Knowledge and Data Engineering (T-KDE), 27(5): 1274-1287, 2015. [Code]
  26. Liangyue Li*, Sheng Li*, Yun Fu: Learning Low-Rank and Discriminative Dictionary for Image Classification. Image and Vision Computing (IVC), 32(10): 814-823, 2014. (* indicates equal contribution) [Code]
  27. Ya Su, Sheng Li, Shengjin Wang, and Yun Fu, Submanifold Decomposition, IEEE Trans. Circuits and Systems for Video Technology (T-CSVT), 24(11): 1885-1897, 2014.
  28. Xiao-Yuan Jing, Sheng Li, David Zhang, Jian Yang, Jing-Yu Yang: Supervised and Unsupervised Parallel Subspace Learning for Large-Scale Image Recognition. IEEE Trans. Circuits System for Video Technology (T-CSVT), 22(10): 1497-1511, 2012.
  29. Xiao-Yuan Jing, Sheng Li, David Zhang, Chao Lan, Jingyu Yang: Optimal Subset-division based Discrimination and Its Kernelization for Face and Palmprint Recognition. Pattern Recognition (PR), 45(10): 3590-3602, 2012.
  30. Xiao-Yuan Jing, Sheng Li, Chao Lan, David Zhang, Jingyu Yang, Qian Liu: Color Image Canonical Correlation Analysis for Face Feature Extraction and Recognition. Signal Processing (SP), 91(8): 2132-2140, 2011.

Conference Papers

  1. Ronghang Zhu and Sheng Li. Self-supervision based Semantic Alignment for Unsupervised Domain Adaptation. SDM, 2022.
  2. Zhixuan, Chu, Stephen Rathbun, and Sheng Li. Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Observational Data. SDM, 2022.
  3. Yue Bai, Zhiqiang Tao, Lichen Wang, Sheng Li, Yu Yin, and Yun Fu. Collaborative Attention Mechanism for Multi-Modal Time Series Classification. SDM, 2022.
  4. Saed Rezayi, Handong Zhao and Sheng Li. XDC: Adversarial Adaptive Cross Domain Face Clustering. AAAI, 2022. (Poster)
  5. Zhixuan Chu, Stephen Rathbun and Sheng Li. Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data. KDD, 2021.
  6. Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan Rossi, Nedim Lipka and Sheng Li. EDGE: Enriching Knowledge Graph Embeddings with External Text. NAACL, 2021.
  7. Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li and Yun Fu. Correlative Channel-Aware Fusion for Multi-View Time Series Classification. AAAI, 2021.
  8. Ronghang Zhu, Zhiqiang Tao, Yaliang Li, and Sheng Li. Automated Graph Learning via Population Based Self-Tuning GCN. SIGIR, 2021.
  9. Liuyi Yao, Yaliang Li, Sheng Li, Mengdi Huai, Jing Gao and Aidong Zhang. SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation. CIKM, 2021.
  10. Ronghang Zhu and Sheng Li. Self-supervised Universal Domain Adaptation with Adaptive Memory Separation. ICDM, 2021.
  11. Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, and Jordan Read. Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. ICDM, 2021.
  12. Ronghang Zhu, Xiaodong Jiang, Jiasen Lu and Sheng Li. Transferable Feature Learning on Graphs Across Visual Domains. IEEE ICME, 2021.
  13. Kang Yuan, Sheng Li. 2.5D Pose Guided Human Image Generation. ACM ICMR, 2021.
  14. Saed Rezayi, Nedim Lipka, Vishwa Vinay, Ryan A. Rossi, Franck Dernoncourt, Tracy H. King, Sheng Li. A Framework for Knowledge-Derived Query Suggestions. IEEE BigData, 2021.
  15. Saed Rezayi, Saber Soleymani, Hamid R. Arabnia and Sheng Li. Socially Aware Multimodal Deep Neural Networks for Fake News Classification. IEEE MIPR, 2021.
  16. Sheng Li and Handong Zhao. A Survey on Representation Learning for User Modeling. IJCAI, 2020.
  17. Zhixuan Chu, Stephen Rathbun and Sheng Li. Matching in Selective and Balanced Representation Space for Treatment Effects Estimation. CIKM, 2020.
  18. Abhilash Dorle, Fangyu Li, Wenzhan Song and Sheng Li. Learning Discriminative Virtual Sequences for Time Series Classification. CIKM, 2020.
  19. Heng-Shiou Sheu and Sheng Li. Context-aware Graph Embedding for Session-based News Recommendation. ACM RecSys, 2020.
  20. Xiaodong Jiang, Pengsheng Ji and Sheng Li. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks. IJCAI, 2019.
  21. Liuyi Yao, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, Aidong Zhang. On the Estimation of Treatment Effect with Text Covariates. IJCAI, 2019.
  22. Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin. Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning. IJCAI, 2019.
  23. Xiaowei Jia, Sheng Li, Handong Zhao, Sungchul Kim and Vipin Kumar. Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? KDD, 2019.
  24. Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao and Yun Fu. Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit. KDD, 2019.
  25. Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai and Mingyang Ling. Scene Graph Generation with External Knowledge and Image Reconstruction. CVPR, 2019.
  26. Xiaowei Jia, Sheng Li, Ankush Khandelwal, Guruprasad Nayak, Anuj Karpatne and Vipin Kumar. Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection. SDM, 2019.
  27. Zheng Zhang, Guosen Xie, Yang Li, Sheng Li and Zi Huang. SADIH: Semantic-Aware DIscrete Hashing. AAAI, 2019.
  28. Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan Rossi and Eunyee Koh. Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior, WSDM, 2019.
  29. Xueyu Mao, Saayan Mitra and Sheng Li. Training Streaming Factorization Machines with Alternating Least Squares. SIGIR, 2019.
  30. Zhao Zhang, Jiahuan Ren, Sheng Li, Richang Hong, Zhengjun Zha and Meng Wang. Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation. ACM MM, 2019.
  31. Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao and Aidong Zhang. ACE: Adaptively Similarity-preserved Representation Learning for Individual Treatment Effect Estimation. ICDM, 2019.
  32. Zhao Zhang, Lei Wang, Yang Wang, Sheng Li, Zheng Zhang, Zhengjun Zha, and Meng Wang. Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery. ICDM, 2019.
  33. Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao and Aidong Zhang. Representation Learning for Treatment Effect Estimation from Observational Data, NIPS, 2018. [Code]
  34. Kai Li, Sheng Li, Zhengming Ding, Weidong Zhang, and Yun Fu. Latent Discriminant Subspace Representations for Multi-view Outlier Detection. AAAI, 2018.
  35. Kai Li, Zhengming Ding, Sheng Li, and Yun Fu. Discriminative Semi-coupled Projective Dictionary Learning for Low-Resolution Person Re-Identification. AAAI, 2018.
  36. Shumin Jing, Sheng Li. Contextual Collaborative Filtering for Student Response Prediction in Mixed-Format Tests. AAAI, 2018. (Poster)
  37. Zhengming Ding, Sheng Li, Ming Shao and Yun Fu. Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation. ECCV, 2018.
  38. Tuan Manh Lai, Trung Bui, Sheng Li. A Review on Deep Learning Techniques Applied to Answer Selection. COLING, 2018.
  39. Sheng Li, Yun Fu. Matching on Balanced Nonlinear Representations for Treatment Effects Estimation. NIPS, 2017.
  40. Sheng Li, Yun Fu. Robust Multi-Label Semi-Supervised Classification. IEEE BigData, 2017.
  41. Sheng Li, Hongfu Liu, Zhiqiang Tao, and Yun Fu. Multi-View Graph Learning with Adaptive Label Propagation. IEEE BigData, 2017. [Code]
  42. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. From Ensemble Clustering to Multi-View Clustering. IJCAI, 2017.
  43. Sheng Li, Nikos Vlassis, Jaya Kawale and Yun Fu. Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. IJCAI, 2016.
  44. Sheng Li. Learning Robust Representations for Data Analytics. IJCAI, 2016. (Poster)
  45. Sheng Li, Yaliang Li and Yun Fu. Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach. CIKM, 2016.
  46. Sheng Li, Yun Fu. Unsupervised Transfer Learning via Low-Rank Coding for Image Clustering, IJCNN, 2016. [Code]
  47. Zhiqiang Tao, Hongfu Liu, Sheng Li and Yun Fu. Robust Spectral Ensemble Clustering. CIKM, 2016.
  48. Hongfu Liu, Ming Shao, Sheng Li and Yun Fu. Infinite Ensemble for Image Clustering. KDD, 2016.
  49. Sheng Li, Kang Li and Yun Fu. Temporal Subspace Clustering for Human Motion Segmentation. ICCV, 2015. [Code]
  50. Sheng Li, Ming Shao and Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. IJCAI, 2015. [Code]
  51. Ming Shao, Sheng Li, Zhengming Ding and Yun Fu. Deep Linear Coding for Fast Graph Clustering. IJCAI, 2015.
  52. Sheng Li, Jaya Kawale and Yun Fu. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. CIKM, 2015.
  53. Sheng Li, Jaya Kawale and Yun Fu. Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization, SIGIR, 2015.
  54. Sheng Li, Ming Shao and Yun Fu. Multi-view Low-Rank Analysis for Outlier Detection. SDM, 2015. [Code]
  55. Sheng Li, Yun Fu, Robust Subspace Discovery through Supervised Low-Rank Constraints, SDM, 2014. (Best Paper Award) [Code]
  56. Kang Li, Sheng Li, and Yun Fu, Early Classification of Ongoing Observation, ICDM, 2014.
  57. Ming Shao, Sheng Li, Tongliang Liu, Dacheng Tao, Thomas S. Huang, and Yun Fu, Learning Relative Features Through Adaptive Pooling for Image Classification, ICME, 2014. (Best Paper Award Candidate)
  58. Sheng Li, Ming Shao, and Yun Fu, Locality Linear Fitting One-class SVM with Low-Rank Constraints for Outlier Detection, IJCNN, 2014.
  59. Sheng Li, Yun Fu. Low-Rank Coding with b-Matching Constraint for Semi-supervised Classification, IJCAI, 2013. [Code]
  60. Liangyue Li, Sheng Li, and Yun Fu. Discriminative Dictionary Learning with Low-Rank Regularization for Face Recognition. IEEE FG, 2013. (Best Student Paper Honorable Mention Award) [Code]

Sponsors