Lijun Zhang's Publications

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

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

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- Non-redundant Multiple Clustering by Nonnegative Matrix Factorization [PDF, Bibtex]

S. Yang*, and**L. Zhang**

Machine Learning, 106(5): 695 - 712, 2017.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- Locally Regressive Projections [PDF, Bibtex]

**L. Zhang**

International Journal of Software and Informatics (**IJSI**), 7(3): 435 - 451, 2013.

- 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.

- 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.

- 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.

- 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.