| 2026 | COLT | Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract). | Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu |
| 2025 | ICLR | How Does Critical Batch Size Scale in Pre-training? | Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade |
| 2025 | ICML | Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. | Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett |
| 2025 | ICML | Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. | Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu |
| 2025 | ICML | Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes. | Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett |
| 2024 | COLT | Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. | Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu |
| 2024 | ICLR | Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. | Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu |
| 2024 | ICLR | How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett |
| 2024 | ICSOC | UELLM: A Unified and Efficient Approach for Large Language Model Inference Serving. | Yiyuan He, Minxian Xu, Jingfeng Wu, Wanyi Zheng, Kejiang Ye, Cheng-Zhong Xu |
| 2023 | ICML | Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2022 | AISTATS | Gap-Dependent Unsupervised Exploration for Reinforcement Learning. | Jingfeng Wu, Vladimir Braverman, Lin Yang |
| 2022 | ICML | Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. | Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2021 | ACML | Lifelong Learning with Sketched Structural Regularization. | Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman |
| 2021 | COLT | Benign Overfitting of Constant-Stepsize SGD for Linear Regression. | Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2021 | ICLR | Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. | Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu |
| 2021 | NSDI | Ship Compute or Ship Data? Why Not Both? | Jie You, Jingfeng Wu, Xin Jin, Mosharaf Chowdhury |
| 2021 | NSDI | Twenty Years After: Hierarchical Core-Stateless Fair Queueing. | Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin |
| 2021 | SIGCOMM | Programmable packet scheduling with a single queue. | Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin |
| 2020 | ICML | Obtaining Adjustable Regularization for Free via Iterate Averaging. | Jingfeng Wu, Vladimir Braverman, Lin Yang |
| 2020 | ICML | On the Noisy Gradient Descent that Generalizes as SGD. | Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu |
| 2019 | CVPR | Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. | Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu |
| 2019 | ICML | The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. | Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma |