Jingwei Li

Jingwei Li (李经纬) IIIS, Tsinghua University Email: ljw22 [at] mails.tsinghua.edu.cn Google Scholar

About Me

I am a fourth-year PhD student(expected graduation: June 2027) at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, advised by Professor Jingzhao Zhang. Previously, I received my B.S. degree in Mathematics from Tsinghua University in 2022.

Research Interests

My research lies at the intersection of theoretical and applied machine learning. On the theoretical side, I am interested in establishing provable guarantees for neural network generalization and control theory. On the applied side, my focus is on pre-training data for large language models (LLMs), such as optimizing data mixtures and exploring their effects on model performance.

Publications

α–β indicates alphabetical author order, * indicates equal contribution.

• Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control [arxiv]
  Hongyi Zhou*, Jingwei Li* , Jingzhao Zhang
  Conference on Neural Information Processing Systems (NeurIPS) 2025
  

• Towards Black-box Membership Inference Attack for Diffusion Models [arxiv]
  Jingwei Li , Jing Dong, Tianxing He, Jingzhao Zhang
  International Conference on Machine Learning (ICML) 2025
  

• Understanding Nonlinear Implicit Bias via Region Counts in Input Space [arxiv]
  Jingwei Li* , Jing Xu*, Zifan Wang, Huishuai Zhang, Jingzhao Zhang
  International Conference on Machine Learning (ICML) 2025
  

• MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory [arxiv]
  Zifan Wang*, Jingwei Li* , Yitang Li, Yunze Liu
  International Conference on Uncertainty in Artificial Intelligence (UAI) 2025
  

• Online Control with Adversarial Disturbance for Continuous-time Linear Systems [arxiv]
  Jingwei Li , Jing Dong, Can Chang, Baoxiang Wang, Jingzhao Zhang
  Conference on Neural Information Processing Systems (NeurIPS) 2024
  

• Online Policy Optimization for Robust Markov Decision Process [arxiv]
  (α–β) Jing Dong, Jingwei Li , Baoxiang Wang, Jingzhao Zhang
  International Conference on Uncertainty in Artificial Intelligence (UAI) 2024
  

• Iteratively Learn Diverse Strategies with State Distance Information [arxiv]
  Wei Fu, Weihua Du* , Jingwei Li* , Sunli Chen, Jingzhao Zhang, Yi Wu
  Conference on Neural Information Processing Systems (NeurIPS) 2023
  

Technical Report

• KIMI K2.5: Visual Agentic Intelligence [arxiv]
  Kimi Team et al.
  

Internship Experience

• Jump Trading 2026.06 - 2026.09
  Quantitative Research Summer Intern (Incoming)
  Selected for the 2026 Quantitative Research Summer Internship Program to work on systematic trading strategy analysis.
• ByteDance (Seed) 2026.03 - 2026.06
  Algorithm Intern at Pre-training Team
  Focused on multi-epoch training for large language models (LLM) mid-training. Worked on developing efficient strategies for optimizing training epoch numbers for each dataset.
• Moonshot AI (KIMI) 2025.07 - 2026.02
  Algorithm Intern at Pre-training Team
  Focused on data mixture for large language models (LLM) mid-training. Worked on developing efficient strategies for optimizing data mixture. Contributed to the development of KIMI K2.5 pre-training phase.
• Microsoft Research Asia (MSRA) 2023.08 - 2023.12
  Research Intern at Machine Learning Theory Team
  Worked on deep learning theory, specifically on measures to describe implicit bias in neural networks. Successfully achieved a correlation of 0.98 between the generalization gap and the measure.