Lijun Zhang's Publications

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- Stochastic Approximation Approaches to Group Distributionally Robust Optimization [arXiv]

**L. Zhang**, P. Zhao, T. Yang, and Z.-H. Zhou

- Non-stationary Online Convex Optimization with Arbitrary Delays [arXiv]

Y. Wan, C. Yao, M. Song, and**L. Zhang**

- 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**

- Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization [arXiv]

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

- Adaptive and Efficient Algorithms for Tracking the Best Expert [arXiv]

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

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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, 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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

- 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.

- 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

- 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.

- $\ell_1$-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.

- 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.

- 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.

- 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.

- 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.

- 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, 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- Online Strongly Convex Optimization with Unknown Delays [PDF, Bibtex]

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

Machine Learning, 111(3): 871 - 893, 2022.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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 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.

- 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.

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

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

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

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- Locally Regressive Projections [PDF, Bibtex]

**L. Zhang**

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

- 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.

- 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.

- 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.

- 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.