Lijun Zhang's Publications

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Preprints

1. Stochastic Approximation Approaches to Group Distributionally Robust Optimization [arXiv]
   L. Zhang, P. Zhao, T. Yang, and Z.-H. Zhou
   
   
2. Non-stationary Online Convex Optimization with Arbitrary Delays [arXiv]
   Y. Wan, C. Yao, M. Song, and L. Zhang
   
   
3. Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees [arXiv]
   Y. Wang, W. Yang, W. Jiang, S. Lu, B. Wang, H. Tang, Y. Wan, and L. Zhang
   
   
4. Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization [arXiv]
   P. Zhao, Y.-J. Zhang, L. Zhang, and Z.-H. Zhou
   
   
5. Adaptive and Efficient Algorithms for Tracking the Best Expert [arXiv]
   S. Lu, and L. Zhang
   

Conference (*student author)

1. Improved Dynamic Regret for Online Frank-Wolfe [arXiv]
   Yuanyu Wan, L. Zhang, and M. Song
   In Proceedings of the 36th Annual Conference on Learning Theory (COLT 2023), to appear, 2023.
   
   
2. Stochastic Graphical Bandits with Heavy-Tailed Rewards
   Y. Gou*, J. Yi, and L. Zhang
   In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), to appear, 2023.
   
   
3. Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization [arXiv]
   S. Chen*, W.-W. Tu, P. Zhao, and L. Zhang
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), to appear, 2023.
   
   
4. Learning Unnormalized Statistical Models via Compositional Optimization
   W. Jiang*, J. Qin, L. Wu, C. Chen, T. Yang, and L. Zhang
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), to appear, 2023.
   
   
5. Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization [arXiv]
   Z.-H. Qiu*, Q. Hu, Z. Yuan, D. Zhou, L. Zhang, and T. Yang
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), to appear, 2023.
   
   
6. Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
   Q. Hu, Z.-H. Qiu, Z. Guo, L. Zhang, and T. Yang
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), to appear, 2023.
   
   
7. Distributed Projection-free Online Learning for Smooth and Convex Losses
   Y. Wang*, Y. Wan, S. Zhang, and L. Zhang
   In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), to appear, 2023.
   
   
8. Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor [PDF, Supplementary, Bibtex]
   L. Zhang, W. Jiang, J. Yi, and T. Yang
   In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 4928 - 4942, 2022.
   
   
9. Online Frank-Wolfe with Arbitrary Delays [PDF, Supplementary, Bibtex]
   Y. Wan*, W.-W. Tu, L. Zhang
   In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 19703 - 19715, 2022.
   
   
10. Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization [PDF, Supplementary, Bibtex]
    W. Jiang*, G. Li, Y. Wang, L. Zhang, and T. Yang
    In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 32499 - 32511, 2022.
    
    
11. Efficient Methods for Non-stationary Online Learning [PDF, Supplementary, Bibtex]
    P. Zhao, Y.-F. Xie, L. Zhang, and Z.-H. Zhou
    In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 11573 - 11585, 2022.
    
    
12. Non-Stationary Dueling Bandits for Online Learning to Rank [PDF, Bibtex]
    S. Lu*, Y. Miao, P. Yang, Y. Hu, and L. Zhang
    In Proceedings of the 6th APWeb and WAIM Joint International Conference on Web and Big Data (APWeb-WAIM 2022), Part II, pages 166 - 174, 2022.
    
    
13. A Simple yet Universal Strategy for Online Convex Optimization [PDF, Bibtex]
    L. Zhang, G. Wang, J. Yi, and T. Yang
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), pages 26605 - 26623, 2022.
    
    
14. Optimal Algorithms for Stochastic Multi-Level Compositional Optimization [PDF, Bibtex]
    W. Jiang*, B. Wang, Y. Wang, L. Zhang, and T. Yang
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), pages 10195 - 10216, 2022.
    
    
15. Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence [PDF, Bibtex]
    Z.-H. Qiu*, Q. Hu, Y. Zhong, L. Zhang, and T. Yang
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), pages 18122 - 18152, 2022.
    
    
16. Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance [PDF, Bibtex]
    Z. Yuan, Y. Wu, Z.-H. Qiu, X. Du, L. Zhang, D. Zhou, and T. Yang
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022), pages 25760 - 25782, 2022.
    
    
17. Adaptive Feature Generation for Online Continual Learning from Imbalanced Data [PDF, Bibtex]
    Y. Jian*, J. Yi, and L. Zhang
    In Advances in Knowledge Discovery and Data Mining (PAKDD 2022), pages 276 - 289, 2022.
    
    
18. Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [PDF, Bibtex]
    G. Wang*, M. Yang, L. Zhang, and T. Yang
    In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), pages 3753 - 3771, 2022.
    
    
19. Non-stationary Continuum-armed Bandits for Online Hyperparameter Optimization [PDF, Bibtex]
    Shiyin Lu*, Yu-Hang Zhou, Jing-Cheng Shi, Wenya Zhu, Qingtao Yu, Qing-Guo Chen, Qing Da, and L. Zhang
    In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022), pages 618 - 627, 2022.
    
    
20. Revisiting Smoothed Online Learning [PDF, Supplementary, Bibtex]
    L. Zhang, W. Jiang, S. Lu, and T. Yang
    In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pages 13599 - 13612, 2021.
    
    
21. Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions [PDF, Supplementary, Bibtex]
    L. Zhang, G. Wang, W.-W. Tu, W. Jiang, and Z.-H. Zhou
    In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pages 24968 - 24980, 2021.
    
    
22. Online Convex Optimization with Continuous Switching Constraint [PDF, Supplementary, Bibtex]
    G. Wang*, Y. Wan, T. Yang, and L. Zhang
    In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pages 28636 - 28647, 2021.
    
    
23. Learning to Augment Imbalanced Data for Re-ranking Models [PDF, Bibtex]
    Z.-H. Qiu*, Y.-C. Jian, Q.-G. Chen, and L. Zhang
    In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), pages 1478 - 1487, 2021.
    
    
24. Deep Unified Cross-Modality Hashing by Pairwise Data Alignment [PDF, Bibtex]
    Y. Wang*, B. Xue, Q. Cheng, Y. Chen, and L. Zhang
    In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), pages 1129 - 1135, 2021.
    
    
25. Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions [PDF, Bibtex]
    P. Zhao, and L. Zhang
    In Proceedings of the 3rd Annual Learning for Dynamics and Control Conference (L4DC 2021), pages 48 - 59, 2021.
    
    
26. Stochastic Graphical Bandits with Adversarial Corruptions [PDF, Bibtex]
    S. Lu*, G. Wang, and L. Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 8749 - 8757, 2021.
    
    
27. Stochastic Bandits with Graph Feedback in Non-Stationary Environments [PDF, Bibtex]
    S. Lu*, Y. Hu, and L. Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 8758 - 8766, 2021.
    
    
28. Approximate Multiplication of Sparse Matrices with Limited Space [PDF, Bibtex]
    Y. Wan*, and L. Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 10058 - 10066, 2021.
    
    
29. Projection-Free Online Learning in Dynamic Environments [PDF, Bibtex]
    Y. Wan*, B. Xue, and L. Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 10067 - 10075, 2021.
    
    
30. Projection-free Online Learning over Strongly Convex Sets [PDF, Bibtex]
    Y. Wan*, and L. Zhang
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pages 10076 - 10084, 2021.
    
    
31. Dynamic Regret of Convex and Smooth Functions [PDF, arXiv, Bibtex]
    P. Zhao, Y.-J. Zhang, L. Zhang, and Z.-H. Zhou
    In Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pages 12510 - 12520, 2020.
    
    
32. Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space [PDF, Bibtex]
    P. Li*, R. Li, Q. Da, A.-X. Zeng, and L. Zhang
    In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), pages 2605 - 2612, 2020.
    
    
33. Searching Privately by Imperceptible Lying: A Novel Private Hashing Method with Differential Privacy [PDF, Bibtex]
    Y. Wang*, S. Lu, and L. Zhang
    In Proceedings of the 28th ACM International Conference on Multimedia (ACM Multimedia 2020), pages 2700 - 2709, 2020.
    
    
34. Piecewise Hashing: A Deep Hashing Method for Large-Scale Fine-Grained Search [PDF, Bibtex]
    Y. Wang*, X.-S. Wei, B. Xue, and L. Zhang
    In Proceedings of the 3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2020), pages 432 - 444, 2020.
    
    
35. 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.
    
    
36. 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.
    
    
37. 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.
    
    
38. 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.
    
    
39. 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.
    
    
40. A Simple Approach for Non-stationary Linear Bandits [PDF, Supplementary, Errata, arXiv, 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.
    
    
41. 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.
    
    
42. An Adversarial Domain Adaptation Network for Cross-Domain Fine-Grained Recognition [PDF, Bibtex]
    Y. Wang*, R.-J. Song, X.-S. Wei, and L. Zhang
    In Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV 2020), pages 1217 - 1225, 2020.
    
    
43. SAdam: A Variant of Adam for Strongly Convex Functions [PDF, Bibtex]
    G. Wang*, S. Lu, Q. Cheng, W.-W. Tu, and L. Zhang
    In International Conference on Learning Representations (ICLR 2020), 2020.
    
    
44. 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.
    
    
45. 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.
    
    
46. Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss [PDF, Bibtex]
    P. Li*, J. Yi, B. Zhou, and L. Zhang
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pages 2909 - 2915, 2019
    
    
47. 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.
    
    
48. 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
    
    
49. 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.
    
    
50. 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.
    
    
51. $\ell_1$-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.
    
    
52. 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.
    
    
53. 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.
    
    
54. Query-Efficient Black-Box Attack by Active Learning [PDF, Bibtex]
    P. Li*, J. Yi, and L. Zhang
    In Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), pages 1200 - 1205, 2018.
    
    
55. 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 5882 - 5891, 2018.
    
    
56. 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.
    
    
57. Efficient Adaptive Online Learning via Frequent Directions [PDF, Bibtex, Journal Version]
    Y. Wan*, N. Wei, and L. Zhang
    In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), pages 2748 - 2754, 2018.
    
    
58. Accelerating Adaptive Online Learning by Matrix Approximation [PDF, Supplementary, Bibtex]
    Y. Wan* and L. Zhang
    In Advances in Knowledge Discovery and Data Mining (PAKDD 2018), pages 405 - 417, 2018.
    
    
59. 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.
    
    
60. Charging Task Scheduling for Directional Wireless Charger Networks [PDF, Bibtex]
    H. Dai, K. Sun, A. X. Liu, L. Zhang, J. Zheng, and G. Chen
    In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018), 2018.
    
    
61. 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.
    
    
62. 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.
    
    
63. Scalable Demand-Aware Recommendation [PDF, Supplementary, Bibtex]
    J. Yi, C.-J. Hsieh, K. R. Varshney, L. Zhang, and Y. Li
    In Advances in Neural Information Processing Systems 30 (NIPS 2017), pages 2409 - 2418, 2017.
    
    
64. 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.
    
    
65. 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.
    
    
66. Semi-Supervised Deep Hashing with a Bipartite Graph [PDF, Bibtex]
    X. Yan*, L. Zhang, and W.-J. Li
    In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pages 3238 - 3244, 2017.
    
    
67. Storage Fit Learning with Unlabeled Data [PDF, Bibtex]
    B.-J. Hou, L. Zhang, and Z.-H. Zhou
    In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pages 1844 - 1850, 2017.
    
    
68. 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.
    
    
69. 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.
    
    
70. 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.
    
    
71. 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.
    
    
72. 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.
    
    
73. 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.
    
    
74. 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.
    
    
75. 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.
    
    
76. 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.
    
    
77. 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.
    
    
78. Fast and Accurate Refined Nystrom Based Kernel SVM [PDF, Bibtex]
    Z. Li, T. Yang, L. Zhang, and R. Jin
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016), pages 1830 - 1836, 2016.
    
    
79. 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.
    
    
80. 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.
    
    
81. An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection [PDF, Bibtex]
    T. Yang, L. Zhang, R. Jin, and S. Zhu
    In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pages 135 - 143, 2015.
    
    
82. 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.
    
    
83. 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.
    
    
84. 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.
    
    
85. 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.
    
    
86. 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.
    
    
87. 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.
    
    
88. 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.
    
    
89. 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.
    
    
90. 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.
    
    
91. 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.
    
    
92. 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.
    
    
93. 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.
    
    
94. Multiple Kernel Learning from Noisy Labels by Stochastic Programming [PDF, Bibtex]
    T. Yang, M. Mahdavi, R. Jin, L. Zhang, and Y. Zhou
    In Proceedings of the 29th International Conference on Machine Learning (ICML 2012), pages 233 - 240, 2012.
    
    
95. 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.
    
    
96. Document Summarization Based on Data Reconstruction [PDF, Bibtex] (AAAI-12 Outstanding Paper Awards)
    Z. He, C. Chen, J. Bu, C. Wang, L. Zhang, D. Cai, and X. He
    In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI 2012), pages 620 - 626, 2012.
    
    
97. 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.
    
    
98. Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content [PDF, Bibtex]
    J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He
    In Proceedings of the 18th ACM International Conference on Multimedia (ACM Multimedia 2010), pages 391 - 400, 2010.
    
    
99. 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.
    
    
100. Modeling Dynamic Multi-Topic Discussions in Online Forums [PDF, Bibtex]
     H. Wu, J. Bu, C. Chen, C. Wang, G. Qiu, L. Zhang, and J. Shen
     In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), pages 1455 - 1460, 2010.
     
     
101. 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.

Journal (*student author)

1. Prediction With Unpredictable Feature Evolution [PDF, Bibtex]
   B.-J. Hou, L. Zhang, and Z.-H. Zhou
   IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(10): 5706 - 5715, 2022.
   
   
2. Efficient Adaptive Online Learning via Frequent Directions [PDF, Bibtex]
   Y. Wan*, and L. Zhang
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(10): 6910 - 6923, 2022.
   
   
3. Strongly Adaptive Online Learning over Partial Intervals [PDF, Supplementary, Bibtex]
   Y. Wan*, W.-W. Tu, and L. Zhang
   Science China Information Sciences (SCIS), 65(10): 202101, 2022.
   
   
4. Projection-free Distributed Online Learning with Sublinear Communication Complexity [PDF, Bibtex]
   Y. Wan*, G. Wang, W.-W. Tu, and L. Zhang
   Journal of Machine Learning Research (JMLR), 23(172): 1 - 53, 2022.
   
   
5. Online Strongly Convex Optimization with Unknown Delays [PDF, Bibtex]
   Y. Wan*, W.-W. Tu, and L. Zhang
   Machine Learning, 111(3): 871 - 893, 2022.
   
   
6. Charging Task Scheduling for Directional Wireless Charger Networks [PDF, Bibtex]
   H. Dai, K. Sun, A. X. Liu, L. Zhang, J. Zheng, and G. Chen
   IEEE Transactions on Mobile Computing (TMC), 20(11): 3163 - 3180, 2021.
   
   
7. Bandit Convex Optimization in Non-stationary Environments [PDF, Bibtex]
   P. Zhao, G. Wang, L. Zhang, and Z.-H. Zhou
   Journal of Machine Learning Research (JMLR), 22(125): 1 - 45, 2021.
   
   
8. Learning with Feature Evolvable Streams [PDF, Bibtex]
   B.-J. Hou, L. Zhang, and Z.-H. Zhou
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 33(6): 2602 - 2615, 2021.
   
   
9. High-dimensional Model Recovery from Random Sketched Data by Exploring Intrinsic Sparsity [PDF, Bibtex]
   T. Yang, L. Zhang, Q. Lin, S. Zhu, and Rong Jin
   Machine Learning, 109(5): 899 - 938, 2020.
   
   
10. 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.
    
    
11. 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.
    
    
12. 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.
    
    
13. A Simple Homotopy Proximal Mapping Algorithm for Compressive Sensing [PDF, Bibtex]
    T. Yang, L. Zhang, R. Jin, S. Zhu, and Z.-H. Zhou
    Machine Learning, 108(6): 1019 - 1056, 2019.
    
    
14. 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.
    
    
15. Non-redundant Multiple Clustering by Nonnegative Matrix Factorization [PDF, Bibtex]
    S. Yang*, and L. Zhang
    Machine Learning, 106(5): 695 - 712, 2017.
    
    
16. 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.
    
    
17. 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.
    
    
18. 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.
    
    
19. Multi-View Concept Learning for Data Representation [PDF, Bibtex]
    Z. Guan, L. Zhang, J. Peng, and J. Fan
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 3016 - 3028, 2015.
    
    
20. Expert Finding for Question Answering via Graph Regularized Matrix Completion [PDF, Bibtex]
    Z. Zhao, L. Zhang, X. He, and W. Ng
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(4): 993 - 1004, 2015.
    
    
21. 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.
    
    
22. Graph-Based Local Concept Coordinate Factorization [PDF, Bibtex]
    P. Li, J. Bu, L. Zhang, and C. Chen
    Knowledge and Information Systems (KAIS), 43(1): 103 - 126, 2015.
    
    
23. Unsupervised Document Summarization from Data Reconstruction Perspective [PDF, Bibtex]
    Z. He, C. Chen, J. Bu, C. Wang, L. Zhang, D. Cai, and X. He
    Neurocomputing, 157: 356 - 366, 2015.
    
    
24. 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.
    
    
25. Locally Regressive Projections [PDF, Bibtex]
    L. Zhang
    International Journal of Software and Informatics (IJSI), 7(3): 435 - 451, 2013.
    
    
26. 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.
    
    
27. 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.
    
    
28. Locally Discriminative Topic Modeling [PDF, Bibtex]
    H. Wu, J. Bu, C. Chen, J. Zhu, L. Zhang, H. Liu, C. Wang, and D. Cai
    Pattern Recognition, 45(1): 617 - 625, 2012.
    
    
29. 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.
    
    
30. 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.
    
    
31. 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.
    
    
32. 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.

中文期刊 (*学生作者)

1. 一种满足差分隐私的图赌博机算法 [PDF]
   卢世银*, 王广辉, 邱梓豪, 张利军
   软件学报, 33(9): 3223 - 3235, 2022.
   
   
2. 一种面向复杂场景的无线通信节点智能适变架构 [PDF]
   尹浩, 魏急波, 赵海涛, 熊俊, 梅锴, 张利军, 任保全, 马东堂
   中国科学: 信息科学, 51(2): 294 - 304, 2021.