Recent Research Highlight: (* Corresponding author) 

• Yuanyuan Liu, Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, and Zhouchen Lin. Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.  (SCI 1区, IF: 16.389,  CCF A)

• Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 32(1): 188-202, 2020.  (SCI 1区, IF: 6.977,  CCF A)

• Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin. Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(9): 2066-2080, 2018.  (SCI 1区, IF: 16.389,  CCF A)

• Kaiwen Zhou, Fanhua Shang*, James Cheng. A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. In: Proceedings of the 35th International Conference on Machine Learning  (ICML), pp. 5975-5984, 2018.  CCF A

• Yuanyuan Liu, Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), pp. 4875–4884, 2017.  CCF A

• Fanhua Shang, Yuanyuan Liu, and James Cheng. Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS, Proceedings of Machine Learning Research), 51: 620-629, 2016.

 

Selected Papers: (* Corresponding author, 2020IF)

• Hongying Liu, Ruyi Luo, Fanhua Shang*, Mantang Niu, Yuanyuan Liu. “Progressive Semantic Matching for Video-Text Retrieval”. To appear in: Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021. (CCF A)

• Hua Huang, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”. To appear in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A)

• Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Pan Zhou and Maoguo Gong. “Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (SCI 1区, IF: 10.451)

• Fanhua Shang, Hua Huang, Jun Fan, Hongying Liu, Yuanyuan Liu, Jianhui Liu. “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”. Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE),  2021. (SCI 1区, IF: 6.977,  CCF A)

• Fanhua Shang, Tao Xu, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong. “Differentially Private ADMM Algorithms for Machine Learning”, IEEE Transactions on Information Forensics and Security (TIFS), conditionally accepted, 2021. (SCI 1区, IF: 7.178, CCF A)

• Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu. “Principal Component Analysis in the Stochastic Differential Privacy Model”. To appear in: Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (CCF B)

• Yuanyuan Liu, Jiacheng Geng, Fanhua Shang*, Hongying Liu, Qi Zhu. “Loopless Variance Reduced Stochastic ADMM forEquality Constrained Problems in IoT Applications”. IEEE Internet of Things Journal, accepted, 2021. (SCI 1区, IF: 9.471)

• Yangyang Li, Lin Kong, Fanhua Shang*, Yuanyuan Liu, Hongying Liu, Zhouchen Lin. Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding. To appear in: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (Clear Accept, CCF A)

• Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang*, Yuanyuan Liu. Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling. To appear in: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A)

• Jianrui Chen, Yanqing Lu, Fanhua Shang, Yuyang Wang. “A fuzzy matrix factor recommendation method with forgetting function and user features”. Applied Soft Computing, 100: 106910, 2021. (SCI 1区, IF: 6.725)

• Ronghua Shang, Lujuan Wang, Fanhua Shang, Licheng Jiao, Yangyang Li. “Dual space latent representation learning for unsupervised feature selection”. Pattern Recognition (PR), 114: 107873, 2021. (SCI 1区, IF: 7.740)

• Yuanyuan Liu, Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, and Zhouchen Lin. Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning. Accepted by IEEE Transactions on Pattern Ansalysis and Machine Intelligence (TPAMI), 2020.  (SCI 1区, IF: 16.389,  CCF A)

• Hengmin Zhang, Feng Qian, Fanhua Shang, Wenli Du, Jianjun Qian, Jian Yang. Global Convergence Guarantees of (A) GIST for a Family of Nonconvex Sparse Learning Problems. IEEE Transactions on Cybernetics, 2020. (SCI 1区, IF: 11.448) 

• Yang Meng, Ronghua Shang, Fanhua Shang, Licheng Jiao, Shuyuan Yang, Rustam Stolkin. Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation. IEEE Transactions on Neural Networks and Learning Systems, 2020. (SCI 1区, IF: 10.451)

• Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip K. Paliwal, Fanhua Shang*. Deep Residual-Dense Lattice Network for Speech Enhancement. In: Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI), 2020. (CCF A)

• Hongying Liu, Fanhua Shang*, Shuyuan Yang, Maoguo Gong, Tianwen Zhu, Licheng Jiao. Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification. Accepted by IEEE Transactions on Neural Networks and Learning Systems, 2019. (SCI 1区, IF: 10.451)

• Yuanyuan Liu, Fanhua Shang* and Licheng Jiao. Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (CCF A)

• Fanhua Shang, BingkunWei, Hongying Liu, Yuanyuan Liu, Jiacheng Zhuo. Efficient Semi-Stochastic Gradient Support Pursuit for Sparsity-Constrained Non-convex Optimization. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI, Workshop of Data Science Meets Optimization), 2019. (CCF A)

• Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao. Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.  (CCF A)

• Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhiquan Luo. Direct Acceleration of SAGA using Sampled Negative Momentum. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

• Hengmin Zhang, Jian Yang, Fanhua Shang, Chen Gong, Zhenyu Zhang. LRR for Subspace Segmentation via Tractable Schatten-p Norm Minimization and Factorization.  IEEE Transactions on Cybernetics, 2019. (SCI 1区, IF: 11.448) PDF

• Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. (SCI 1区, IF: 6.977,  CCF A) PDF

• Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin. ASVRG: Accelerated Proximal SVRG. In: Proceedings of Machine Learning Research (PMLR), 2018.  PDF

• Kaiwen Zhou, Fanhua Shang*, James Cheng. A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. In: Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. (CCF A)  PDF

• Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin. Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. (SCI 1区, IF: 16.389,  CCF A) PDF

• Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan. Fuzzy Double Trace Norm Minimization for Recommendation Systems. IEEE Transactions on Fuzzy Systems, 2018. (SCI 1区, IF: 12.029) PDF

• Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K.W. Ng, Yuichi Yoshida. Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS, Journal of Machine Learning Research (CCF A) ), 2018. PDF

• Yuanyuan Liu, Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), 2017. (CCF A) PDF

• Fan Yang, Fanhua Shang, Yuzhen Huang, James Cheng, Jinfeng Li, Yunjian Zhao, Ruihao Zhao. LFTF: A Framework for Efficient Tensor Analytics at Scale. In: Proceedings of the 43rd International Conference on Very Large Data Bases (VLDB), 2017. (CCF A) PDF

• Yuanyuan Liu, Fanhua Shang*, and James Cheng. Accelerated Variance Reduced Stochastic ADMM. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017. (CCF A) PDF

• Fanhua Shang, Yuanyuan Liu, and James Cheng. Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS, Journal of Machine Learning Research, (CCF A) ), 2016. PDF

• Fanhua Shang, Yuanyuan Liu, and James Cheng. Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016. (CCF A) PDF

• Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, and Hong Cheng. Robust Bilinear Factorization with Missing and Grossly Corrupted Observations. Information Sciences,53-72, 2016. (SCI 1区, IF: 6.795)  PDF

• Yuanyuan Liu, Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng. Generalized Higher-Order Orthogonal Iteration for Tensor Learning and Decomposition. IEEE Transactions on Neural Networks and Learning Systems, 27, 2551-2563, 2016. (SCI 1区, IF: 10.451) PDF

• Yuanyuan Liu, Fanhua Shang*, Licheng Jiao, James Cheng, and Hong Cheng.Trace Norm Regularized CANDECOMP/PARAFAC Decomposition with Missing Data. IEEE Transactions on Cybernetics, 45, 2437-2448, 2015. (SCI 1区, IF: 11.448) PDF
• Fanhua Shang, Yuanyuan Liu, and James Cheng. Generalized Higher-Order Tensor Decomposition via Parallel ADMM. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014. (CCF A) PDF

• Yuanyuan Liu, Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng. Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion. In: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), 1763-1771, 2014. (CCF A) PDF

• Yuanyuan Liu, Fanhua Shang*, Hong Cheng, and James Cheng. Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), 2014. (CCF B) PDF

• Fei Yin, Licheng Jiao, Fanhua Shang, Lin Xiong, Xiaodong Wang. Sparse regularization discriminant analysis for face recognition. Neurocomputing, 128, 341-362, 2014. (SCI, IF: 5.719)

• Yuanyuan Liu, Fanhua Shang*, Hong Cheng, James Cheng, and Hanghang Tong. Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM), 2014. (CCF B) PDF

• Fanhua Shang, Yuanyuan Liu, and James Cheng, Hong Cheng. Robust Principal Component Analysis with Missing Data. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), 2014. (CCF B) PDF

• Fanhua Shang, Yuanyuan Liu, James Cheng, and Hong Cheng. Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization. In: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM), 2014. (CCF B) PDF

• Fei Yin, Licheng Jiao, Fanhua Shang, Lin Xiong, Shasha Mao. Double linear regressions for single labeled image per person face recognition. Pattern Recognition, 47(4),1547-1558,  2014. (SCI 1 区, IF: 7.740)

• Jing Chai, Hongtao Chen, Lixia Huang, Fanhua Shang. Maximum margin multiple-instance feature weighting. Pattern Recognition, 47(6), 2091-2103, 2014. (SCI 1 区, IF: 7.740)

• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering. Pattern Recognition, 2013. (SCI 1 区, IF: 7.740) PDF

• Fei Yin, Licheng Jiao, Fanhua Shang, Shuang Wang, Biao Hou. Fast Fisher Sparsity Preserving Projections. Neural Computing and Applications, 23(3-4) ,691-705, 2013. (SCI, IF: 5.605)

• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang*. An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization. Neural Networks, 2013. (SCI1区, IF: 8.050) PDF

• Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Hanghang Tong. Semi-Supervised Learning with Nuclear Norm Regularization. Pattern Recognition, 2013. (SCI 1 区, IF: 7.740) PDF

• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. A Fast Tri-Factorization Method for Low-Rank Matrix Recovery and Completion. Pattern Recognition, 2013. (SCI 1 区, IF: 7.740) PDF

• Fanhua Shang, Licheng Jiao, and Fei Wang. Graph Dual Regularization Non-Negative Matrix Factorization for Co-Clustering. Pattern Recognition, 2012. (SCI 1 区, IF: 7.740) PDF

• Fanhua Shang, Licheng Jiao, and Fei Wang. Semi-Supervised Learning with Mixed Knowledge Information. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2012. (CCF A) PDF

• Licheng Jiao, Fanhua Shang*, Fei Wang, and Yuanyuan Liu. Fast Semi-Supervised Clustering with Enhanced Spectral Embedding. Pattern Recognition, 2012. (SCI 1 区, IF: 7.740) PDF

• Fanhua Shang, Licheng Jiao, Jiarong Shi, and Fei Wang, Maoguo Gong. Fast Affinity Propagation Clustering: A Multilevel Approach. Pattern Recognition, 2012. (SCI 1 区, IF: 7.740) PDF

• Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Fei Wang. Learning Spectral Embedding via Iterative Eigenvalue Thresholding. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), 2012. (CCF B)

• Fanhua Shang, Licheng Jiao, Jiarong Shi, Maoguo Gong, and R. H. Shang. Fast Density-Weighted Low-Rank Approximation Spectral Clustering. Data Mining and Knowledge Discovery,23,345-378,2011. (SCI, IF: 3.670) PDF

• Fanhua Shang, Yuanyuan Liu, Fei Wang. Learning Spectral Embedding for Semi-Supervised Clustering. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), 2011. (CCF B) PDF