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: Machine learning (unsupervised learning, transfer learning, deep learning) with applications to big data analytics, computer vision, causal inference, user modeling, natural language processing, bioinformatics, etc.

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...]

  • 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.
  • 10/2020, One paper on protein kinase inhibitor response prediction is accepted by BMC Bioinformatics.
  • 09/2020, One paper on co-embedding of nodes and edges with graph neural networks is accepted by IEEE Trans. PAMI.
  • 08/2020, One paper on graph embedding for session-based news recommendation is accepted at ACM RecSys 2020.
  • 08/2020, I accepted the invitations to serve as Senior Program Committee member of IJCAI 2021 and AAAI 2021.
  • 07/2020, Two papers on causal inference and virtual sequence learning are accepted at CIKM 2020.
  • 07/2020, I gave a keynote talk at the International Conference on Neural Computing for Advanced Applications (NCAA 2020).
  • 06/2020, Received a Pilot Grant (Co-PI) from Georgia CTSA to support our research on AI for mobile health.
  • 05/2020, I received the Aharon Katzir Young Investigator Award from the International Neural Network Society (INNS).
  • 05/2020, I received the Faculty Research Excellence Award from the Department of Computer Science.
  • 04/2020, One survey paper on representation learning for user modeling is accepted at IJCAI 2020.
  • 03/2020, One paper that develops machine learning classifiers to predict donor specificity is accepted by eLife.
  • 03/2020, I will co-organize the Second International Workshop on Bringing Semantic Knowledge into Vision and Text Understanding, at IJCAI 2020.
  • 03/2020, I accepted the invitation to serve as Program Committee member of NeurIPS 2020, ECCV 2020, UAI 2020, and EMNLP 2020.
  • 02/2020, I accepted the invitation to serve as Area Chair of ICPR 2020.
  • 12/2019, I received the M. G. Michael Award for Sciences from the Franklin College of Arts and Sciences, UGA.
  • 11/2019, I accepted the invitation to serve as Senior Program Committee member of IJCAI 2020 and PC member of ICML 2020 and KDD 2020.
  • 10/2019, I accepted the invitation to serve as Program Committee member of CVPR 2020, ECAI 2020, PAKDD 2020, and IEEE MIPR 2020.
  • 09/2019, I will co-organize a tutorial on representation learning for causal inference at AAAI 2020.
  • 09/2019, Three journal papers are accepted by IEEE Trans. KDE, IEEE Systems Journal, and Multimedia Tools and Applications.
  • 08/08/2019, Two papers on low-rank coding and causal inference are accepted at ICDM 2019 (Acceptance Rate: 18.5%).
  • 07/25/2019, I gave an invited talk on graph neural networks at the Adobe Data Science Symposium Webinar.
  • 07/17/2019, I accepted the invitation to serve as Program Committee member of ICLR 2020.
  • 07/17/2019, One paper on analysis–synthesis dictionary learning is accepted at Neural Networks.
  • 07/02/2019, One paper on robust subspace discovery is accepted at ACM MM 2019.
  • 06/21/2019, I accepted the invitation to serve as Senior Program Committee member of AAAI 2020.
  • 05/10/2019, Three papers on graph neural networks, causal inference, and dictionary learning are accepted at IJCAI 2019 (Acceptance Rate: 17.9%).
  • 04/29/2019, Two papers on sequence modeling and adversarial training are accepted at KDD 2019 (Acceptance Rate: 14%).
  • 04/14/2019, One short paper on factorization machines is accepted at SIGIR 2019 (Acceptance Rate: 24.4%).
  • 04/01/2019, I gave an invited talk on machine learning and deep learning at the College of Education, The University of Iowa, IA.
  • 03/16/2019, I accepted the invitation to serve as Program Committee member of IEEE BigData 2019.
  • 03/11/2019, One paper on multi-view ensemble clustering is accepted by IEEE Trans. NNLS.
  • 03/06/2019, I received the Adobe Data Science Research Award.
  • 03/06/2019, I accepted the invitation to serve as Program Committee member of UAI 2019.
  • 03/05/2019, I am elevated to be Senior Member of IEEE.
  • 02/24/2019, One paper on scene graph generation is accepted at CVPR 2019 (Acceptance Rate: 25.2%).

Awards and Honors

  • 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 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: ICPR (2020), VCIP (2017)
  • SPC Member: IJCAI (2020-2021), AAAI (2019-2021)
  • PC Member: NeurIPS (2019-2020), ICML (2019-2021), CVPR (2019-2021), ACL (2021), 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. 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.
  2. 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]
  3. 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.
  4. 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.
  5. 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.
  6. 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)
  7. 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.
  8. 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.
  9. 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.
  10. 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]
  11. 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]
  12. 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.
  13. 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]
  14. Hongfu Liu, Ming Shao, Sheng Li, and Yun Fu. Infinite Ensemble Clustering. Data Mining and Knowledge Discovery (DMKD), 32(2): 385-416, 2018.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Kang Li, Sheng Li, Sangmin Oh, Yun Fu. Videography based Unconstrained Video Analysis, IEEE Trans. Image Processing (T-IP), 26(5): 2261-2273, 2017.
  20. 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.
  21. 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]
  22. 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]
  23. 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]
  24. 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.
  25. 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.
  26. 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.
  27. 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. Zhixuan Chu, Stephen Rathbun and Sheng Li. Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data. KDD, 2021.
  2. Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan Rossi, Nedim Lipka and Sheng Li. EDGE: Enriching Knowledge Graph Embeddings with External Text. NAACL, 2021.
  3. Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li and Yun Fu. Correlative Channel-Aware Fusion for Multi-View Time Series Classification. AAAI, 2021.
  4. Ronghang Zhu, Zhiqiang Tao, Yaliang Li, and Sheng Li. Automated Graph Learning via Population Based Self-Tuning GCN. SIGIR, 2021.
  5. Ronghang Zhu, Xiaodong Jiang, Jiasen Lu and Sheng Li. Transferable Feature Learning on Graphs Across Visual Domains. IEEE ICME, 2021.
  6. Kang Yuan, Sheng Li. 2.5D Pose Guided Human Image Generation. ACM ICMR, 2021.
  7. Saed Rezayi, Saber Soleymani, Hamid R. Arabnia and Sheng Li. Socially Aware Multimodal Deep Neural Networks for Fake News Classification. IEEE MIPR, 2021.
  8. Sheng Li and Handong Zhao. A Survey on Representation Learning for User Modeling. IJCAI, 2020.
  9. Zhixuan Chu, Stephen Rathbun and Sheng Li. Matching in Selective and Balanced Representation Space for Treatment Effects Estimation. CIKM, 2020.
  10. Abhilash Dorle, Fangyu Li, Wenzhan Song and Sheng Li. Learning Discriminative Virtual Sequences for Time Series Classification. CIKM, 2020.
  11. Heng-Shiou Sheu and Sheng Li. Context-aware Graph Embedding for Session-based News Recommendation. ACM RecSys, 2020.
  12. Xiaodong Jiang, Pengsheng Ji and Sheng Li. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks. IJCAI, 2019.
  13. Liuyi Yao, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, Aidong Zhang. On the Estimation of Treatment Effect with Text Covariates. IJCAI, 2019.
  14. Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin. Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning. IJCAI, 2019.
  15. 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.
  16. 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.
  17. Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai and Mingyang Ling. Scene Graph Generation with External Knowledge and Image Reconstruction. CVPR, 2019.
  18. 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.
  19. Zheng Zhang, Guosen Xie, Yang Li, Sheng Li and Zi Huang. SADIH: Semantic-Aware DIscrete Hashing. AAAI, 2019.
  20. 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.
  21. Xueyu Mao, Saayan Mitra and Sheng Li. Training Streaming Factorization Machines with Alternating Least Squares. SIGIR, 2019.
  22. 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.
  23. 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.
  24. 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.
  25. 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]
  26. Kai Li, Sheng Li, Zhengming Ding, Weidong Zhang, and Yun Fu. Latent Discriminant Subspace Representations for Multi-view Outlier Detection. AAAI, 2018.
  27. Kai Li, Zhengming Ding, Sheng Li, and Yun Fu. Discriminative Semi-coupled Projective Dictionary Learning for Low-Resolution Person Re-Identification. AAAI, 2018.
  28. Shumin Jing, Sheng Li. Contextual Collaborative Filtering for Student Response Prediction in Mixed-Format Tests. AAAI, 2018. (Poster)
  29. Zhengming Ding, Sheng Li, Ming Shao and Yun Fu. Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation. ECCV, 2018.
  30. Tuan Manh Lai, Trung Bui, Sheng Li. A Review on Deep Learning Techniques Applied to Answer Selection. COLING, 2018.
  31. Sheng Li, Yun Fu. Matching on Balanced Nonlinear Representations for Treatment Effects Estimation. NIPS, 2017.
  32. Sheng Li, Yun Fu. Robust Multi-Label Semi-Supervised Classification. IEEE BigData, 2017.
  33. Sheng Li, Hongfu Liu, Zhiqiang Tao, and Yun Fu. Multi-View Graph Learning with Adaptive Label Propagation. IEEE BigData, 2017. [Code]
  34. Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. From Ensemble Clustering to Multi-View Clustering. IJCAI, 2017.
  35. Sheng Li, Nikos Vlassis, Jaya Kawale and Yun Fu. Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. IJCAI, 2016.
  36. Sheng Li. Learning Robust Representations for Data Analytics. IJCAI, 2016. (Poster)
  37. Sheng Li, Yaliang Li and Yun Fu. Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach. CIKM, 2016.
  38. Sheng Li, Yun Fu. Unsupervised Transfer Learning via Low-Rank Coding for Image Clustering, IJCNN, 2016. [Code]
  39. Zhiqiang Tao, Hongfu Liu, Sheng Li and Yun Fu. Robust Spectral Ensemble Clustering. CIKM, 2016.
  40. Hongfu Liu, Ming Shao, Sheng Li and Yun Fu. Infinite Ensemble for Image Clustering. KDD, 2016.
  41. Sheng Li, Kang Li and Yun Fu. Temporal Subspace Clustering for Human Motion Segmentation. ICCV, 2015. [Code]
  42. Sheng Li, Ming Shao and Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. IJCAI, 2015. [Code]
  43. Ming Shao, Sheng Li, Zhengming Ding and Yun Fu. Deep Linear Coding for Fast Graph Clustering. IJCAI, 2015.
  44. Sheng Li, Jaya Kawale and Yun Fu. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. CIKM, 2015.
  45. Sheng Li, Jaya Kawale and Yun Fu. Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization, SIGIR, 2015.
  46. Sheng Li, Ming Shao and Yun Fu. Multi-view Low-Rank Analysis for Outlier Detection. SDM, 2015. [Code]
  47. Sheng Li, Yun Fu, Robust Subspace Discovery through Supervised Low-Rank Constraints, SDM, 2014. (Best Paper Award) [Code]
  48. Kang Li, Sheng Li, and Yun Fu, Early Classification of Ongoing Observation, ICDM, 2014.
  49. 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)
  50. Sheng Li, Ming Shao, and Yun Fu, Locality Linear Fitting One-class SVM with Low-Rank Constraints for Outlier Detection, IJCNN, 2014.
  51. Sheng Li, Yun Fu. Low-Rank Coding with b-Matching Constraint for Semi-supervised Classification, IJCAI, 2013. [Code]
  52. 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