Zijian Liu

Email: zl3067 at stern dot nyu dot edu

[Google Scholar][CV]

About Me

I am currently a Ph.D. candidate majoring in Operations Management in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. My advisor is Zhengyuan Zhou.

Experience

Research Interests

Mathematical Optimization, Theoretical Machine Learning

Research Work

Publications

1. Random Reshuffling Dominates Stochastic Gradient Descent
   Zijian Liu
   In Proceedings of the 39th Annual Conference on Learning Theory (COLT 2026)
   [arXiv][Proceedings]
2. Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
   Zijian Liu
   In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026) (To Appear)
   [arXiv][OpenReview]
3. Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis
   Zijian Liu
   In the 14th International Conference on Learning Representations (ICLR 2026)
   [arXiv (Full Version)][OpenReview]
4. Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications
   Zijian Liu
   In Proceedings of the 37th International Conference on Algorithmic Learning Theory (ALT 2026)
   [arXiv (Full Version)][OpenReview][Proceedings]
5. Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
   Zijian Liu, Zhengyuan Zhou
   In Proceedings of the 42nd International Conference on Machine Learning (ICML 2025)
   [arXiv][OpenReview][Proceedings]
6. Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
   Zijian Liu, Zhengyuan Zhou
   In the 13th International Conference on Learning Representations (ICLR 2025)
   [arXiv][OpenReview][Proceedings]
7. On the Last-Iterate Convergence of Shuffling Gradient Methods
   Zijian Liu, Zhengyuan Zhou
   In Proceedings of the 41st International Conference on Machine Learning (ICML 2024) (Oral Presentation)
   [arXiv][OpenReview][Proceedings]
8. On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity
   Junyi Fan*, Yuxuan Han*, Zijian Liu, Jian-Feng Cai, Yang Wang, Zhengyuan Zhou
   In Proceedings of the 41st International Conference on Machine Learning (ICML 2024)
   [OpenReview][Proceedings]
9. Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
   Zijian Liu, Zhengyuan Zhou
   In the 12th International Conference on Learning Representations (ICLR 2024)
   [arXiv (Full Version)][OpenReview][Proceedings]
10. Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise
    Zijian Liu, Jiawei Zhang, Zhengyuan Zhou
    In Proceedings of the 36th Annual Conference on Learning Theory (COLT 2023)
    [arXiv][Proceedings]
11. High Probability Convergence of Stochastic Gradient Methods
    Zijian Liu*, Ta Duy Nguyen*, Thien Hang Nguyen*, Alina Ene, Huy L. Nguyen
    In Proceedings of the 40th International Conference on Machine Learning (ICML 2023)
    [arXiv (Full Version)][OpenReview][Proceedings]
12. On the Convergence of AdaGrad(Norm) on \(\mathbb{R}^{d}\): Beyond Convexity, Non-Asymptotic Rate and Acceleration
    Zijian Liu*, Ta Duy Nguyen*, Alina Ene, Huy L. Nguyen
    In the 11th International Conference on Learning Representations (ICLR 2023)
    [arXiv][OpenReview]
13. Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
    Zijian Liu*, Ta Duy Nguyen*, Alina Ene, Huy L. Nguyen
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
    [arXiv][Proceedings]
14. Distributionally Robust \(Q\)-Learning
    Zijian Liu, Qinxun Bai, Jose Blanchet, Perry Dong, Wei Xu, Zhengqing Zhou, Zhengyuan Zhou
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
    [Proceedings]

Manuscripts

1. Product Return as a Sequence of Search Processes: Optimality and Search Duration
   Xiaoyu Fan\(^\dagger\), Srikanth Jagabathula\(^\dagger\), Zijian Liu\(^\dagger\), Eitan Muller\(^\dagger\)
   Major Revision at Operations Reseaerch, 2025
   [Available upon Request]
2. Adam Converges in Nonsmooth Nonconvex Optimization
   Zijian Liu
   In Submission, 2026
   [arXiv]
3. In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
   Zijian Liu
   In Submission, 2026
   [arXiv]
4. Near-Optimal Non-Convex Stochastic Optimization under Generalized Smoothness
   Zijian Liu, Srikanth Jagabathula, Zhengyuan Zhou
   Manuscript, 2023
   [arXiv]
5. Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation
   Zijian Liu, Zhengyuan Zhou
   Manuscript, 2023
   [arXiv]
6. META-STORM: Generalized Fully-Adaptive Variance Reduced SGD for Unbounded Functions
   Zijian Liu*, Ta Duy Nguyen*, Thien Hang Nguyen*, Alina Ene, Huy L. Nguyen
   Manuscript, 2022
   [arXiv]