Preprints

1. Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings. [arXiv]
   W. Jiang, and L. Zhang
   
   
2. Improved Analysis for Sign-based Methods with Momentum Updates. [arXiv]
   W. Jiang, D. Yu, S. Yang, W. Yang, and L. Zhang
   
   
3. Optimizing Unnormalized Statistical Models through Compositional Optimization.
   W. Jiang, J. Qin, L. Wu, C. Chen, T. Yang, and L. Zhang
   
   
4. Non-Stationary Projection-Free Online Learning with Dynamic Regret Guarantees.
   Y. Wang, H. Bai, W. Jiang, W. Yang, Y. Wan, and L. Zhang
   
   
5. Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions. [arXiv]
   L. Zhang, W. Yang, G. Wang, W. Jiang, and Z.-H. Zhou
   
   

Journal

1. Revisiting Stochastic Multi-Level Compositional Optimization. [PDF]
   W. Jiang, S. Yang, Y. Wang, T. Yang, and L. Zhang
   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2025), 47(7): 5613 - 5624, 2025.
   
   
2. Normalized Adaptive Variance Reduction Method. [PDF]
   W. Jiang, S. Yang, Y. Wang, and L. Zhang
   Journal of Software, 2025.
   
   

Conference

1. Smoothed Online Convex Optimization with Delayed Feedback
   S. Yang, W. Yang, W. Jiang, Y. Wan, and L. Zhang
   In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025), to appear, 2025.
   
   
2. Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions [PDF]
   W. Jiang, S. Yang, Y. Wang, and L. Zhang
   In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 22047 - 22080, 2024.
   
   
3. Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction [PDF]
   W. Jiang, S. Yang, W. Yang, and L. Zhang
   In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pages 33891 - 33932, 2024.
   
   
4. Online Composite Optimization Between Stochastic and Adversarial Environments [PDF]
   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.
   
   
5. Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization [PDF]
   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.
   
   
6. Small-loss Adaptive Regret for Online Convex Optimization [PDF]
   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.
   
   
7. Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond [PDF]
   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.
   
   
8. Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees [PDF, 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.
   
   
9. Learning Unnormalized Statistical Models via Compositional Optimization [PDF]
   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.
   
   
10. Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization [PDF, Supplementary]
    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. Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor [PDF, Supplementary]
    L. Zhang, W. Jiang, J. Yi, and T. Yang
    In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 4928 - 4942, 2022.
    
    
12. Optimal Algorithms for Stochastic Multi-Level Compositional Optimization [PDF]
    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.
    
    
13. Revisiting Smoothed Online Learning [PDF, Supplementary]
    L. Zhang, W. Jiang, S. Lu, and T. Yang
    In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pages 13599 - 13612, 2021.
    
    
14. Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions [PDF, Supplementary]
    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.