Lijun Zhang's Publications

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

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

- Stochastic Optimization for Non-convex Inf-Projection Problems [PDF, Supplementary, Bibtex]

Y. Yan, Y. Xu,**L. Zhang**, X. Wang, and T. Yang

In Proceedings of the 37th International Conference on Machine Learning (**ICML 2020**), pages 10660 - 10669, 2020.

- VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning [PDF, Bibtex]

F. Shang, K. Zhou, H. Liu, J. Cheng, I. W. Tsang,**L. Zhang**, D. Tao, and L. Jiao

IEEE Transactions on Knowledge and Data Engineering (**TKDE**), 32(1): 188 - 202, 2020.

- Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate [PDF, Bibtex]

**L. Zhang**, and Z.-H. Zhou

In Proceedings of the 32nd Annual Conference on Learning Theory (**COLT 2019**), pages 3160 - 3179, 2019

- $\ell_1$-regression with Heavy-tailed Distributions [PDF, Bibtex, arXiv]

**L. Zhang**, and Z.-H. Zhou

In Advances in Neural Information Processing Systems 31 (**NeurIPS 2018**), pages 1076 - 1086, 2018.

- Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions [PDF, Supplementary, Bibtex]

M. Liu, X. Zhang,**L. Zhang**, R. Jin, and T. Yang

In Advances in Neural Information Processing Systems 31 (**NeurIPS 2018**), pages 4678 - 4689, 2018.

- A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer [PDF, Bibtex]

T. Yang, Z. Li, and**L. Zhang**

In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (**AISTATS 2018**), pages 445 - 453, 2018.

- 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 and 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**), pages 1305 - 1320, 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.

- Learning with Feature Evolvable Streams

B.-J. Hou,**L. Zhang**, and Z.-H. Zhou

IEEE Transactions on Knowledge and Data Engineering (**TKDE**), in press, 2020.

- Dynamic Regret of Convex and Smooth Functions [arXiv]

P. Zhao, Y.-J. Zhang,**L. Zhang**, and Z.-H. Zhou

In Advances in Neural Information Processing Systems 33 (**NeurIPS 2020**), to appear, 2020.

- Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity [PDF, Supplementary, Bibtex]

Y. Wan*, W.-W. Tu, and**L. Zhang**

In Proceedings of the 37th International Conference on Machine Learning (**ICML 2020**), pages 9818 - 9828, 2020.

- Online Learning in Changing Environments [PDF, Bibtex]

**L. Zhang**

In Proceedings of the 29th International Joint Conference on Artificial Intelligence (**IJCAI 2020**), Early Career, pages 5178 - 5182, 2020.

- Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs [PDF, Bibtex, arXiv]

B. Xue*, G. Wang, Y. Wang, and**L. Zhang**

In Proceedings of the 29th International Joint Conference on Artificial Intelligence (**IJCAI 2020**), pages 2936 - 2942, 2020.

- Minimizing Dynamic Regret and Adaptive Regret Simultaneously [PDF, Bibtex, arXiv]

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

In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (**AISTATS 2020**), pages 309 - 319, 2020.

- A Simple Approach for Non-stationary Linear Bandits [PDF, Supplementary, Bibtex]

P. Zhao,**L. Zhang**, Y. Jiang, and Z.-H. Zhou

In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (**AISTATS 2020**), pages 746 - 755, 2020.

- Bandit Convex Optimization in Non-stationary Environments [PDF, Bibtex, arXiv]

P. Zhao, G. Wang,**L. Zhang**, and Z.-H. Zhou

In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (**AISTATS 2020**), pages 1508 - 1518, 2020.

- SAdam: A Variant of Adam for Strongly Convex Functions [PDF, Bibtex]

G. Wang*, S. Lu, W. Tu, and**L. Zhang**

In International Conference on Learning Representations (**ICLR 2020**), 2020.

- Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization [PDF, Bibtex]

G. Wang*, S. Lu, Y. Hu, and**L. Zhang**

In Proceedings of the 34th AAAI Conference on Artificial Intelligence (**AAAI 2020**), pages 6162 - 6169, 2020.

- Accelerating Adaptive Online Learning by Matrix Approximation[PDF, Bibtex]

Y. Wan*, and**L. Zhang**

International Journal of Data Science and Analytics (**JDSA**), 9(4): 389 - 400, 2020.

- Multi-Objective Generalized Linear Bandits [PDF, Bibtex]

S. Lu*, G. Wang, Y. Hu, and**L. Zhang**

In Proceedings of the 28th International Joint Conference on Artificial Intelligence (**IJCAI 2019**), pages 3080 - 3086, 2019.

- Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization [PDF, Supplementary, Bibtex]

G. Wang*, S. Lu, and**L. Zhang**

In Proceedings of 35th Conference on Uncertainty in Artificial Intelligence (**UAI 2019**), pages 659 - 668, 2019.

- Adaptive Regret of Convex and Smooth Functions [PDF, Bibtex, arXiv]

**L. Zhang**, T.-Y. Liu, and Z.-H. Zhou

In Proceedings of the 36th International Conference on Machine Learning (**ICML 2019**), pages 7414 - 7423, 2019.

- Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards [PDF, Supplementary, Bibtex]

S. Lu*, G. Wang, Y. Hu, and**L. Zhang**

In Proceedings of the 36th International Conference on Machine Learning (**ICML 2019**), pages 4154 - 4163, 2019.

- Adaptive Online Learning in Dynamic Environments [PDF, Bibtex, arXiv]

**L. Zhang**, S. Lu, and Z.-H. Zhou

In Advances in Neural Information Processing Systems 31 (**NeurIPS 2018**), pages 1323 - 1333, 2018.

- Dynamic Regret of Strongly Adaptive Methods [PDF, Supplementary, Bibtex]

**L. Zhang**, T. Yang, R. Jin, and Z.-H. Zhou

In Proceedings of the 35th International Conference on Machine Learning (**ICML 2018**), pages 5877 - 5886, 2018.

- Minimizing Adaptive Regret with One Gradient per Iteration [PDF, Bibtex]

G. Wang*, D. Zhao, and**L. Zhang**

In Proceedings of the 27th International Joint Conference on Artificial Intelligence (**IJCAI 2018**), pages 2762 - 2768, 2018.

- Efficient Adaptive Online Learning via Frequent Directions [PDF, Bibtex]

Y. Wan*, N. Wei, and**L. Zhang**

In Proceedings of the 27th International Joint Conference on Artificial Intelligence (**IJCAI 2018**), pages 2748 - 2754, 2018.

- Accelerating Adaptive Online Learning by Matrix Approximation[PDF, Supplementary, Bibtex]

Y. Wan* and**L. Zhang**

In Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (**PAKDD 2018**), pages 405 - 417, 2018.

- Improved Dynamic Regret for Non-degenerate Functions [PDF, Supplementary, Bibtex]

**L. Zhang**, T. Yang, J. Yi, R. Jin, and Z.-H. Zhou

In Advances in Neural Information Processing Systems 30 (**NIPS 2017**), pages 732 - 741, 2017.

- Learning with Feature Evolvable Streams [PDF, Supplementary, Bibtex]

B.-J. Hou,**L. Zhang**, and Z.-H. Zhou

In Advances in Neural Information Processing Systems 30 (**NIPS 2017**), pages 1416 - 1426, 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**), pages 392 - 401, 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**), pages 449 - 457, 2016.

- Online Kernel Learning with Nearly Constant Support Vectors [PDF, Bibtex]

M. Lin,**L. Zhang**, R. Jin, S. Weng, and C. Zhang

Neurocomputing, 179: 26 - 36, 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**), pages 305 - 314, 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.

- Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion [PDF, Bibtex]

**L. Zhang**, T. Yang, R. Jin, and Z.-H. Zhou

Journal of Machine Learning Research (**JMLR**), 20(97):1 - 22, 2019.

- 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**), pages 820 - 828, 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**), pages 658 - 666, 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 and 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 and 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.