I am a fourth-year year Ph.D. student majoring in computer science at the Institute for Interdisciplinary Information Sciences in Tsinghua University. I am very fortunate to be advised by Professor Andrew Chi-Chih Yao, who is the A.M. Turing laureate of 2000. I received my B.S. degree in artificial intelligence from Peking University in 2021, advised by Professor Liwei Wang.
My research focuses on machine learning, both on theoretical side and application side. I enjoy establishing theoretical guarantees of generalization and optimization of deep learning algorithms. My current research focuses on designing practical and theoretically-sound optimization algorithms to pretrain and finetune large language models. I have previously worked on topics including generalization, adversarial robustness, federated learning and differential privacy.
Publications
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems
Published in Neurips, 2024
Huaqing Zhang*, Lesi Chen*, Jing Xu, Jingzhao Zhang
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
Published in ICML, 2024
Jing Xu, Jingzhao Zhang
On Bilevel Optimization without Lower-level Strong Convexity
Published in COLT, 2024
Lesi Chen*, Jing Xu*, JingZhao Zhang
Towards Data-Algorithm Dependent Generalization Analysis: a Case Study on Overparameterized Linear Regression
Published in Neurips, 2023
Jing Xu*, Jiaye Teng*, Yang Yuan, Andrew C Yao
Quantifying the Variability Collapse of Neural Networks
Published in ICML, 2023
Jing Xu*, Haoxiong Liu*
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Published in ICML, 2023
Lesi Chen, Jing Xu, Luo Luo
Preprints
FedCM: Federated Learning with Client-level Momentum
Jing Xu, Sen Wang, Liwei Wang, Andrew C Yao
Scalable Model Merging with Progressive Layer-wise Distillation
Jing Xu, Jiazheng Li, Jingzhao Zhang
Understanding Nonlinear Implicit Bias via Region Counts in Input Space
Jingwei Li*, Jing Xu*, Zifan Wang, Huishuai Zhang, Jingzhao Zhang