Mirror Descent Under Generalized Smoothness [arXiv]
D. Yu, W. Jiang, Y. Wan, and L. Zhang
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond [PDF, arXiv] L. Zhang, H. Bai, P. Zhao, T. Yang, and Z.-H. Zhou
Universal Online Convex Optimization Meets Second-order Bounds [PDF, arXiv] L. Zhang, Y. Wang, G. Wang, J. Yi, and T. Yang
Projection-free Online Learning with Arbitrary Delays [arXiv]
Y. Wan, Y. Wang, C. Yao, W.-W. Tu, and L. Zhang
Adaptive and Efficient Algorithms for Tracking the Best Expert [arXiv]
S. Lu, and L. Zhang
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning [arXiv]
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In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025), to appear, 2025.
Revisiting Projection-Free Online Learning with Time-Varying Constraints [PDF, Bibtex]
Y. Wang*, Y. Wan, and L. Zhang
In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025), pages 21339 - 21347, 2025.
Towards Unbiased Information Extraction and Adaptation in Cross-Domain Recommendation [PDF, Bibtex]
Y. Wang*, Y. Jian, W. Yang, S. Lu, L. Shen, B. Wang, X. Zeng, and L. Zhang
In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025), pages 12757 - 12765, 2025.
Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting [PDF, Bibtex]
S. Yang*, Y. Wan, and L. Zhang
In Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025), pages 21992 - 22000, 2025.
Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees [PDF, Bibtex]
S. Chen*, Y. Wang, Y.-F. Wu, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 106555 - 106581, 2024.
Universal Online Convex Optimization with 1 Projection per Round [PDF, Bibtex]
W. Yang*, Y. Wang, P. Zhao, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 31438 - 31472, 2024.
Online Composite Optimization Between Stochastic and Adversarial Environments [PDF, Bibtex]
Y. Wang*, S. Chen, W. Jiang, W. Yang, Y. Wan, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 94808 - 94850, 2024.
Online Non-convex Learning in Dynamic Environments [PDF, Bibtex]
Z. Xu*, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 51930 - 51962, 2024.
Improved Regret for Bandit Convex Optimization with Delayed Feedback [PDF, Bibtex]
Y. Wan, C. Yao, M. Song, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 169 - 196, 2024.
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions [PDF, Bibtex]
W. Jiang*, S. Yang, Y. Wang, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 22047 - 22080, 2024.
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction [PDF, Bibtex]
W. Jiang*, S. Yang, W. Yang, and L. Zhang
In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 33891 - 33932, 2024.
Nearly Optimal Regret for Decentralized Online Convex Optimization [PDF, Bibtex]
Y. Wan, T. Wei, M. Song, and L. Zhang
In Proceedings of the 37th Annual Conference on Learning Theory (COLT 2024), pages 4862 - 4888, 2024.
Efficient Stochastic Approximation of Minimax Excess Risk Optimization [PDF, Bibtex, arXiv] L. Zhang, H. Bai, W.-W. Tu, P. Yang, and Y. Hu
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 58599 - 58630, 2024.
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond [PDF, Bibtex]
D. Yu*, Y. Cai, W. Jiang, and L. Zhang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 57384 - 57414, 2024.
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization [PDF, Bibtex]
W. Jiang*, S. Yang, W. Yang, Y. Wang, Y. Wan, and L. Zhang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 21962 - 21987, 2024.
High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails [PDF, Bibtex]
L. Liu*, Y. Wang, and L. Zhang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 32122 - 32138, 2024.
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO [PDF, Bibtex]
Z.-H. Qiu*, S. Guo, M. Xu, T. Zhao, L. Zhang, and T. Yang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 41604 - 41643, 2024.
Small-loss Adaptive Regret for Online Convex Optimization [PDF, Bibtex]
W. Yang*, W. Jiang, Y. Wang, P. Yang, Y. Hu, and L. Zhang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 56156 - 56195, 2024.
Non-stationary Online Convex Optimization with Arbitrary Delays [PDF, Bibtex]
Y. Wan, C. Yao, M. Song, and L. Zhang
In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), pages 49991 - 50011, 2024.
Soft Contrastive Learning for Implicit Feedback Recommendations [PDF, Bibtex]
Z. Zhuang*, and L. Zhang
In Advances in Knowledge Discovery and Data Mining (PAKDD 2024), pages 219 - 230, 2024.
Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning [PDF, Bibtex]
W. Yang*, Y. Jian, Y. Wang, S. Lu, L. Shen, B. Wang, H. Tang, and L. Zhang
In Proceedings of the ACM Web Conference 2024 (WWW 2024), pages 3195 - 3206, 2024.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees [PDF, Bibtex, arXiv]
Y. Wang*, W. Yang, W. Jiang, S. Lu, B. Wang, H. Tang, Y. Wan, and L. Zhang
In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI 2024), pages 15671 - 15679, 2024.
Stochastic Approximation Approaches to Group Distributionally Robust Optimization [PDF, Bibtex, arXiv] L. Zhang, P. Zhao, Z.-H. Zhuang, T. Yang, and Z.-H. Zhou
In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pages 52490 - 52522, 2023.
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards [PDF, Supplementary, Bibtex, arXiv]
B. Xue, Y. Wang, Y. Wan, J. Yi, and L. Zhang
In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pages 70880 - 70891, 2023.
NeCa: Network Calibration for Class Incremental Learning [PDF, Bibtex]
Z. Zhang*, and L. Zhang
In Pattern Recognition (ACPR 2023), pages 385 - 399, 2023.
Improved Dynamic Regret for Online Frank-Wolfe [PDF, Bibtex]
Y. Wan, L. Zhang, and M. Song
In Proceedings of the 36th Annual Conference on Learning Theory (COLT 2023), pages 3304 - 3327, 2023.
Stochastic Graphical Bandits with Heavy-Tailed Rewards [PDF, Supplementary, Bibtex]
Y. Gou*, J. Yi, and L. Zhang
In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), pages 734 - 744, 2023.
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization [PDF, Bibtex, Journal Version]
S. Chen*, W.-W. Tu, P. Zhao, and L. Zhang
In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), pages 5002 - 5035, 2023.
Learning Unnormalized Statistical Models via Compositional Optimization [PDF, Bibtex]
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), pages 15105 - 15124, 2023.
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization [PDF, Bibtex]
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), pages 28389 - 28421, 2023.
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization [PDF, Bibtex]
Q. Hu, Z.-H. Qiu, Z. Guo, L. Zhang, and T. Yang
In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), pages 13550 - 13583, 2023.
Distributed Projection-free Online Learning for Smooth and Convex Losses [PDF, Bibtex]
Y. Wang*, Y. Wan, S. Zhang, and L. Zhang
In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), pages 10226 - 10234, 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, and 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, Journal Version]
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, Journal Version]
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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
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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]
S. Lu*, Y.-H. Zhou, J.-C. Shi, W. Zhu, Q. Yu, Q.-G. Chen, Q. 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, arXiv] 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
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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
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Projection-Free Online Learning in Dynamic Environments [PDF, Bibtex]
Y. Wan*, B. Xue, and L. Zhang
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Projection-free Online Learning over Strongly Convex Sets [PDF, Bibtex]
Y. Wan*, and L. Zhang
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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
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Online Learning in Changing Environments [PDF, Bibtex] L. Zhang
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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
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A Simple Approach for Non-stationary Linear Bandits [PDF, Supplementary, Errata, arXiv, Bibtex]
P. Zhao, L. Zhang, Y. Jiang, and Z.-H. Zhou
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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
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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
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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, Journal Version]
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
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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
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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]
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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
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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.
Continual Learning with Unknown Task Boundary [PDF, Bibtex]
X. Zhu*, J. Yi, and L. Zhang
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press, 2024.
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization [PDF, Bibtex]
P. Zhao, Y.-J. Zhang, L. Zhang, and Z.-H. Zhou
Journal of Machine Learning Research (JMLR), 25(98): 1 - 52, 2024.
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.
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.
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.
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.