Lijun Zhang's Publications

Stochastic Optimization | Convex Optimization | Online Learning | Randomized Algorithm | Compressive Sensing

Clustering | Active Learning | Dimensionality Reduction | LAMDA Publications | Home


Stochastic Optimization

  1. Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n^2)-type of Risk Bounds [PDF, Bibtex]
    L. Zhang, T. Yang, and R. Jin
    In Proceedings of the 30th Annual Conference on Learning Theory (COLT 2017), pages 1954 - 1979, 2017.

  2. Sparse Learning with Stochastic Composite Optimization [PDF, Bibtex]
    W. Zhang, L. Zhang, Z. Jin, R. Jin, D. Cai, X. Li, R. Liang, and X. He
    IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 39(6): 1223 - 1236, 2017.

  3. Efficient Stochastic Optimization for Low-Rank Distance Metric Learning [PDF, Supplementary, Bibtex]
    J. Zhang*, and L. Zhang
    In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017), pages 933 - 939, 2017.

  4. A Two-stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis [PDF, Bibtex]
    Z. Li, T. Yang, L. Zhang, and R. Jin
    In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017), pages 2224 - 2230, 2017.

  5. Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections [PDF, Supplementary, Bibtex]
    J. Chen, T. Yang, Q. Lin, L. Zhang, and Y. Chang
    In Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), pages 122 - 131, 2016.

  6. Stochastic Optimization for Kernel PCA [PDF, Supplementary, Bibtex]
    L. Zhang, T. Yang, J. Yi, R. Jin, and Z.-H. Zhou
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016), pages 2316 - 2322, 2016.

  7. Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization [PDF, Errata, Bibtex]
    M. Mahdavi, L. Zhang, and R. Jin
    In Proceedings of the 28th Conference on Learning Theory (COLT 2015), 2015.

  8. Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD) [PDF, Bibtex]
    Q. Qian, R. Jin, J. Yi, L. Zhang, and S. Zhu
    Machine Learning, 99(3): 353 - 372, 2015.

  9. Sparse Learning for Stochastic Composite Optimization [PDF, Bibtex]
    W. Zhang, L. Zhang, Y. Hu, R. Jin, D. Cai, and X. He
    In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pages 893 - 899, 2014.

  10. Linear Convergence with Condition Number Independent Access of Full Gradients [PDF, Bibtex]
    L. Zhang, M. Mahdavi, and R. Jin
    In Advances in Neural Information Processing Systems 26 (NIPS 2013), pages 980 - 988, 2013.

  11. Mixed Optimization for Smooth Functions [PDF, Supplementary, Bibtex]
    M. Mahdavi, L. Zhang, and R. Jin
    In Advances in Neural Information Processing Systems 26 (NIPS 2013), pages 674 - 682, 2013.

  12. O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions [PDF, Supplementary, Bibtex]
    L. Zhang, T. Yang, R. Jin, and X. He
    In Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pages 1121 - 1129, 2013.

Convex Optimization

  1. A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates [PDF, Bibtex, Full Version]
    T. Yang, Q. Lin, and L. Zhang
    In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pages 3901 - 3910, 2017.

  2. SVD-free Convex-Concave Approaches for Nuclear Norm Regularization [PDF, Supplementary, Bibtex]
    Y. Xiao*, Z. Li, T. Yang, and L. Zhang
    In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pages 3126 - 3132, 2017.

Online Learning

  1. Online Stochastic Linear Optimization under One-bit Feedback [PDF, Supplementary, Bibtex]
    L. Zhang, T. Yang, R. Jin, Y. Xiao, and Z.-H. Zhou
    In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), 2016.

  2. Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient [PDF, Supplementary, Bibtex]
    T. Yang, L. Zhang, R. Jin, and J. Yi
    In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), 2016.

  3. Online Bandit Learning for a Special Class of Non-convex Losses [PDF, Supplementary, Bibtex]
    L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou
    In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015), pages 3158 - 3164, 2015.

  4. Online Kernel Learning with a Near Optimal Sparsity Bound [PDF, Supplementary, Bibtex]
    L. Zhang, J. Yi, R. Jin, M. Lin, and X. He
    In Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pages 621 - 629, 2013.

  5. Efficient Online Learning for Large-Scale Sparse Kernel Logistic Regression [PDF, Bibtex]
    L. Zhang, R. Jin, C. Chen, J. Bu, and X. He
    In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI 2012), pages 1219 - 1225, 2012.

Randomized Algorithm

  1. Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee [PDF, Bibtex]
    Y. Xu, H. Yang, L. Zhang, and T. Yang
    In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017), pages 2796 - 2802, 2017.

  2. Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach [PDF, Supplementary, Bibtex, Full Version]
    L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou
    In Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT 2016), pages 83 - 97, 2016.

  3. Accelerated Sparse Linear Regression via Random Projection [PDF, Bibtex]
    W. Zhang, L. Zhang, R. Jin, D. Cai, and X. He
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016), pages 2337 - 2343, 2016.

  4. Theory of Dual-Sparse Regularized Randomized Reduction [PDF, Bibtex]
    T. Yang, L. Zhang, R. Jin, and S. Zhu
    In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), 2015.

  5. Random Projections for Classification: A Recovery Approach [PDF, Bibtex]
    L. Zhang, M. Mahdavi, R. Jin, T. Yang, and S. Zhu
    IEEE Transactions on Information Theory (TIT), 60(11): 7300 - 7316, 2014.

  6. Recovering the Optimal Solution by Dual Random Projection [PDF, Bibtex, Journal Version]
    L. Zhang, M. Mahdavi, R. Jin, T. Yang, and S. Zhu
    In Proceedings of the 26th Conference on Learning Theory (COLT 2013), pages 135 - 157, 2013.

Compressive Sensing

  1. A Simple Homotopy Algorithm for Compressive Sensing [PDF, Supplementary, Bibtex]
    L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou
    In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015), pages 1116 - 1124, 2015.

  2. Efficient Algorithms for Robust One-bit Compressive Sensing [PDF, Supplementary, Bibtex]
    L. Zhang, J. Yi, and R. Jin
    In Proceedings of the 31st International Conference on Machine Learning (ICML 2014), 2014.

Clustering

  1. Non-redundant Multiple Clustering by Nonnegative Matrix Factorization [PDF, Bibtex]
    S. Yang*, and L. Zhang
    Machine Learning, 106(5): 695 - 712, 2017.

  2. An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints [PDF, Bibtex]
    J. Yi, L. Zhang, T. Yang, W. Liu, and J. Wang
    In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), pages 1405 - 1414, 2015.

  3. A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data [PDF, Supplementary, Bibtex]
    J. Yi, L. Zhang, J. Wang, R. Jin, and A. Jain
    In Proceedings of the 31st International Conference on Machine Learning (ICML 2014), 2014.

  4. Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion [PDF, Bibtex]
    J. Yi, L. Zhang, R. Jin, Q. Qian, and A. Jain
    In Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pages 1400 - 1408, 2013.

  5. Locally Discriminative Coclustering [PDF, Bibtex]
    L. Zhang, C. Chen, J. Bu, Z. Chen, D. Cai, and J. Han
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(6): 1025 - 1035, 2012.

Active Learning

  1. A Unified Feature and Instance Selection Framework Using Optimum Experimental Design [PDF, Bibtex]
    L. Zhang, C. Chen, J. Bu, and X. He
    IEEE Transactions on Image Processing (TIP), 21(5): 2379 - 2388, 2012.

  2. Active Learning Based on Locally Linear Reconstruction [PDF, Appendix, Bibtex]
    L. Zhang, C. Chen, J. Bu, D. Cai, X. He, and T. Huang
    IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 33(10): 2026 - 2038, 2011.

  3. G-Optimal Design with Laplacian Regularization [PDF, Bibtex]
    C. Chen, Z. Chen, J. Bu, C. Wang, L. Zhang, and C. Zhang
    In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), pages 413 - 418, 2010.

  4. Convex Experimental Design Using Manifold Structure for Image Retrieval [PDF, Bibtex]
    L. Zhang, C. Chen, W. Chen, J. Bu, D. Cai, and X. He
    In Proceedings of the 17th ACM International Conference on Multimedia (ACM Multimedia 2009), pages 45 - 53, 2009.

Dimensionality Reduction

  1. A-Optimal Projection for Image Representation [PDF, Appendix, Bibtex]
    X. He, C. Zhang, L. Zhang, and X. Li
    IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 38(5): 1009 - 1015, 2016.

  2. Graph Regularized Feature Selection with Data Reconstruction [PDF, Bibtex]
    Z. Zhao, X. He, D. Cai, L. Zhang, W. Ng, and Y. Zhuang
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(3): 689 - 700, 2016.

  3. Locally Regressive Projections [PDF, Bibtex]
    L. Zhang
    International Journal of Software and Informatics (IJSI), 7(3): 435 - 451, 2013.

  4. Graph Regularized Sparse Coding for Image Representation [PDF, Bibtex]
    M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, and D. Cai
    IEEE Transactions on Image Processing (TIP), 20(5): 1327 - 1336, 2011.

  5. Robust Non-negative Matrix Factorization [PDF, Bibtex]
    L. Zhang, Z. Chen, M. Zheng, and X. He
    Frontiers of Electrical and Electronic Engineering in China, 6(2): 192 - 200, 2011.

  6. Discriminative Codeword Selection for Image Representation [PDF, Bibtex]
    L. Zhang, C. Chen, J. Bu, Z. Chen, S. Tan, and X. He
    In Proceedings of the 18th ACM International Conference on Multimedia (ACM Multimedia 2010), pages 173 - 182, 2010.

  7. Constrained Laplacian Eigenmap for Dimensionality Reduction [PDF, Bibtex]
    C. Chen, L. Zhang, J. Bu, C. Wang, and W. Chen
    Neurocomputing, 73(4-6): 951 - 958, 2010.