Publications

* denotes equal contribution

2026

  1. EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
    Deyao Zhu, Xin Zhou, Shengling Qin, Xuekai Zhu, Hangliang Ding, Shu Zhong , Zixin Wen, Zhonglin Xie, Chenhui Gou, Linxuan Ren, and 37 more authors
    arXiv preprint, arXiv: 2607.05155, 2026
  2. DRAW: Domain Weight Randomization with Bayesian Updating for LLM Pre-Training
    Ruonan Wang, Yongqi Qiao, Zhonglin Xie, and Kun Yuan
    Transactions on Machine Learning Research (TMLR), 2026

2025

  1. Accelerating Optimization via Differentiable Stopping Time
    Zhonglin Xie, Yiman Fong , Haoran Yuan, and Zaiwen Wen
    Advances in Neural Information Processing Systems 38 (NeurIPS 2025), Spotlight, 2025
  2. Accelerated Natural Gradient Method for Parametric Manifold Optimization
    Chenyi Li*, Shuchen Zhu*Zhonglin Xie, and Zaiwen Wen
    arXiv preprint, arXiv: 2504.05753, 2025
  3. OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
    Hongliang Lu*Zhonglin Xie*, Yaoyu Wu, Can Ren, Yuxuan Chen, and Zaiwen Wen
    Forty-Second International Conference on Machine Learning (ICML), 2025
  4. ODE-based Learning to Optimize
    Zhonglin XieWotao Yin, and Zaiwen Wen
    Mathematical Programming, 2025

2023

  1. DAC
    LRSDP: Low-Rank SDP for Triple Patterning Lithography Layout Decomposition
    Yu Zhang*, Yifan Chen*Zhonglin Xie, Hong Xu, Zaiwen WenYibo Lin, and Bei Yu
    In 60th ACM/IEEE Design Automation Conference, DAC 2023, San Francisco, CA, USA, July 9-13, 2023, 2023

2021

  1. Joint Bandwidth Allocation and Path Selection in WANs with Path Cardinality Constraints
    Jinxin Wang, Fan Zhang, Zhonglin XieZaiwen Wen, and Gong Zhang
    J. Commun. Inf. Networks, 2021