姜伟 Wei Jiang Ph.D. student, LAMDA Group Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China Google Scholar

Supervisor

      Professor Lijun Zhang

Education Experiences

• 2020.09 - Now, Ph.D., Computer Science and Technology, Nanjing University.

  • 2023.10 - 2024.05, Visiting Student, National University of Singapore.

• 2016.09 - 2020.06, B.E., Computer Science and Technology (Honors Science Program), Xi'an Jiaotong University.

  • 2019.01 - 2019.06, Exchange Student, University of California, Berkeley. [certificate][transcript]
  • 2018.07 - 2018.08, Exchange Student, University of Manchester. [certificate]

Research Interests

      My research interests include Machine Learning, Stochastic Optimization and Online learning.

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), in press, 2025.
   
   
2. Normalized Adaptive Variance Reduction Method.
   W. Jiang, S. Yang, Y. Wang, and L. Zhang
   Journal of Software, to appear, 2025.
   
   

Conference

1. 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.
   
   
2. 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.
   
   
3. 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.
   
   
4. 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.
   
   
5. 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.
   
   
6. 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.
   
   
7. 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.
   
   
8. Learning Unnormalized Statistical Models via Compositional Optimization [PDF]
   W. Jiang, J. Qin, L. Wu, C. Chen, T. Yang, L. Zhang
   In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), pages 15105 - 15124, 2023.
   
   
9. 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.
   
   
10. 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.
    
    
11. 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.
    
    
12. 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.
    
    
13. 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.
    
    

Honors and Awards

• National Scholarship, 2024
• Excellent Student of Nanjing University, 2024
• NeurIPS Top Reviewer, 2024
• National Scholarship, 2023
• Excellent Student of Nanjing University, 2023
• LAMDA Elite Award, 2023
• Tencent Scholarship, 2022
• Excellent Student of Nanjing University, 2022
• Industrial Bank Scholarship, 2021
• Excellent Student of Nanjing University, 2021
• Grand Champion of DeeCamp Artificial Intelligence Camp, 2020 (￥100,000)
• Excellent Graduate of Xi'an jiaotong University, 2020

Foundation

Distributed Optimization of Compositional Loss Functions. Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_0231) 2024.05-2025.05

Academic Service

• Area Chair: NeurIPS 2025.
• Reviewer for Conferences: ICML 2025,2024,2023,2022; NeurIPS 2024,2023,2022; ICLR 2025,2024; AAAI 2025; AISTATS 2023.
• Reviewer for Journal: IEEE Transactions on Information Forensics & Security; IEEE Transactions on Evolutionary Computation; Machine Learning; Information Sciences; Neurocomputing; TMLR.

Teaching Assistant

• Computational Methods (With Prof. Lijun Zhang; For Undergraduate Student, Spring, 2023)
• Optimization Methods (With Prof. Lijun Zhang; For Graduate Student, Fall, 2021)
• Optimization Methods (With Prof. Lijun Zhang; For Undergraduate Student, Fall, 2020)

Correspondence

• Email: jiangw@lamda.nju.edu.cn

• Office: Room 912, Computer Science Building, Xianlin Campus of Nanjing University