Wu, Jingfeng

31 publications

ICML 2025 Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression Jingfeng Wu, Peter Bartlett, Matus Telgarsky, Bin Yu

ICML 2025 Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes Ruiqi Zhang, Jingfeng Wu, Peter Bartlett

ICLR 2025 How Does Critical Batch Size Scale in Pre-Training? Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean Foster, Sham M. Kakade

ICML 2025 Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter Bartlett

NeurIPS 2025 Improved Scaling Laws in Linear Regression via Data Reuse Licong Lin, Jingfeng Wu, Peter Bartlett

NeurIPS 2025 Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression Jingfeng Wu, Pierre Marion, Peter Bartlett

NeurIPSW 2024 How Does Critical Batch Size Scale in Pre-Training? Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean Foster, Sham M. Kakade

ICLR 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter Bartlett

NeurIPS 2024 In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett

COLT 2024 Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

NeurIPS 2024 Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett

ICLR 2024 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee

JMLR 2023 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

ICML 2023 Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

CoLLAs 2023 Fixed Design Analysis of Regularization-Based Continual Learning Haoran Li, Jingfeng Wu, Vladimir Braverman

NeurIPS 2023 Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability Jingfeng Wu, Vladimir Braverman, Jason Lee

NeurIPS 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman

NeurIPSW 2023 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

AISTATS 2022 Gap-Dependent Unsupervised Exploration for Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin Yang

ICML 2022 Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2022 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression Under Covariate Shift Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2021 Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin Yang

COLT 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade

ICLR 2021 Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

ACML 2021 Lifelong Learning with Sketched Structural Regularization Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman

NeurIPS 2021 The Benefits of Implicit Regularization from SGD in Least Squares Problems Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham Kakade

ICML 2020 Obtaining Adjustable Regularization for Free via Iterate Averaging Jingfeng Wu, Vladimir Braverman, Lin Yang

ICML 2020 On the Noisy Gradient Descent That Generalizes as SGD Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

ICML 2019 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