部分代表论文（* 通讯作者）：

• Yuanyuan Liu, Fanhua Shang, Lin Kong, Licheng Jiao, Zhouchen Lin. "Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(12): 4242-4255, 2021. (SCI 1区, IF: 24.314, 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: 24.314, CCF A)
• Yuanyuan Liu, Fanhua Shang, Weixin An, Zhouchen Lin. “Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots”. In: Proceedings of International Conference on Machine Learning (ICML), pp. 14008-14035, 2022. (CCF A)
• Yuanyuan Liu, Jiacheng Geng, Fanhua Shang, Weixin An,et al,“Laplacian Smoothing Stochastic ADMMs with Differential Privacy Guarantees”. IEEE Transactions on Information Forensics and Security (TIFS), 17: 1814-1826, 2022. (SCI 1区, IF: 7.231, CCF A)
• 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 (IOT), 9(3): 2293-2303, 2022. (SCI 1区, IF: 10.238)
• Weixin An（学生）, Yingjie Yue（学生）, Yuanyuan Liu*, Fanhua Shang, Hongying Liu. “A Numerical DEs Perspective on Unfolded Linearized ADMM Networks for Inverse Problems.” To appear in Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), 2022. (CCF A)
• Yangyang Li（学生）, Lin Kong, Fanhua Shang*, Yuanyuan Liu*, Hongying Liu, Zhouchen Lin. “Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding”. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), pp. 8501-8509, 2021. (CCF A)
• Dong Wang, Yicheng Liu, Liangji Fang, Fanhua Shang*, Yuanyuan Liu*, Hongying Liu. “Balanced Gradient Penalty Improves Deep Long-Tailed Learning.” To appear in Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), 2022. (CCF A)
• Fanhua Shang, Hua Huang, Jun Fan, Yuanyuan Liu*, Hongying Liu, Jianhui Liu. “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (SCI 1区, IF: 9.235, CCF A)
• Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu. “HNO: High-order Numerical Architecture for ODE-Inspired Deep Unfolding Networks”. In: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), pp. 7220-7228, 2022. (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), 16: 4733-4745, 2021. (SCI 1区, IF: 7.231, 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”. To appear in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (SCI 1区, IF: 14.255)
• Hua Huang, Fanhua Shang, Yuanyuan Liu, Hongying Liu. “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2556-2562, 2021. (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, 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)

2018年以前部分代表论文

• Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, and Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS), pp. 4875-4884, 2017. (CCF A).
• Yuanyuan Liu, Fanhua Shang, and James Cheng. Accelerted Variance Reduced Stochastic ADMM.  In: Proceedings of the 31st AAAI Conference on Artificial Intelligence（AAAI）, pp. 2287-2293 ,  2017. (CCF A).
• Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, and Hong Cheng. Generalized Higher-Order Orthogonal Iteraton for Tensor Learning and Decomposition. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 27, no. 12, pp. 2551-2563, 2016. (SCI 1区, IF: 14.255).
• Yuanyuan Liu, Fanhua Shang, Licheng Jiao, James Cheng, and Hong Cheng. Trace Norm Regularized CANDECOMP/PARAFAC Decoposition with Missing Data.  IEEE Transaction on Cybernetics (TCBY), vol. 45, no. 11, pp. 2437-2448, 2015. ( SCI 1区, IF: 19.118).
• 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 (NeurIPS), pp.1763-1771, 2014. (CCF A).
• Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan. Fuzzy Double Trace Norm Minimization for Recommendation Systems.  IEEE Transactions on Fuzzy Systems (TFS) , 2017. (SCI 1区, IF: 12.415).
• Fanhua Shang, Yuanyuan Liu*, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, and Yuichi Yoshida. Guaranteed Sufficie Decrease for Stochastic Variance Reduced Gradient Optimization.  In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
• 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), 2016.
• Fanhua Shang,Yuanyuan Liu, James Cheng. Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016. (CCF A).　
• 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),  pp.1279-1285,2014. (CCF A)．
• Fanhua Shang, Yuanyuan Liu*, Hanghang Tong, James Cheng, and Hong Cheng. Robust Bilinear Factorization with Missing and Grossly Corrupted Observation. Information Sciences. vol. 370, pp. 53-72, 2015. (SCI 1区 ).　
• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering. Pattern Recognition, vol. 46, no.1, pp. 284-292, 2013.( SCI 1区, IF: 8.518 ).　
• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. A Fast Tri-Factorization Method for Low-Rank Matrix Representation for Subspace Clustering. Pattern Recognition. vol. 46, no. 1, pp. 284-292, 2013.( SCI 1区, IF: 8.518 ).　
• Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization. Neural Networkds, vol. 48, pp. 8-18, 2013. (SCI2区, IF: 9.657).
• Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Hanghang Tong. Semi-Supervised Learning with Nuclear Norm Regularization. Pattern Recognition, 2013. (SCI 1区, IF: 8.518) .
• Licheng Jiao,Fanhua Shang, Fei Wang, and Yuanyuan Liu. Fast Semi-Supervised Clustering with Enhanced Spectral Embedding. Pattern Recognition, 2012. ( SCI 1区, IF: 8.518)