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学术论文

 

代表性成果(* 通讯作者):

  • Fanhua Shang, Yuanyuan Liu*, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida, "Guaranteed Sufficie Decrease for Stochastic Variance Reduced Gradient Optimization",  AISTATS, 2018.
  • Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao, "Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds". NIPS, 2017.
  • Yuanyuan Liu, Fanhua Shang, James Cheng, "Accelerted Variance Reduced Stochastic ADMM". AAAI, 2017
  • Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng, "Generalized Higher-Order Orthogonal Iteraton for Tensor Learning and Decomposition", IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 12, pp. 2551-2563, 2016.
  • Yuanyuan Liu, Fanhua Shang, Licheng Jiao, James Cheng, Hong Cheng, "Trace Norm Regularized CANDECOMP/PARAFAC Decoposition with Missing Data", IEEE Transaction on Cybernetics, Vol. 45, no. 11, pp. 2437-2448, 2015.
  • Fanhua Shang*, Yuanyuan Liu*, James Cheng, Zhi-Quan Luo, and Zhouchen Lin, "Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications". To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
  • Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan, "Fuzzy Double Trace Norm Minimization for Recommendation Systems". To appear in IEEE Transactions on Fuzzy Systems, 2017.
  • Fanhua Shang,  Yuanyuan Liu, 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
  • Yuanyuan Liu,Fanhua Shang,Wei Fan, James Cheng, 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
  • 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
  • Fanhua Shang, Yuanyuan Liu*, Hanghang Tong, James Cheng, Hong Cheng, "Robust Bilinear Factorization with Missing and Grossly Corrupted Observation", Information Sciences. vol. 370, pp. 53-72, 2015.
  • Yuanyuan Liu, Fanhua Shang  , Hong Cheng, James Cheng, 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
  • Yuanyuan Liu, Fanhua Shang, Hong Cheng, James Cheng. "Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds". In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), 2014
  • Yuanyuan Liu, Licheng Jiao, 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.
  • Yuanyuan Liu, Licheng Jiao, Fanhua Shang, "An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization", Neural Networkds, vol. 48, pp. 8-18, 2013.
  • Yuanyua Liu, Licheng Jiao, Fanhua Shang, "An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering", Pattern Recognition, vol. 46, no.1, pp. 284-292, 2013.
  • Yuanyuan LiuFanhua Shang. "An Efficient Matrix Factorization Method for Tensor Completion". IEEE Signal Process. Lett.20(4), 307-310, 2013.
  • 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.

 

 

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