Research on human body composition prediction model based on Akaike Information Criterion and improved entropy method. - Bo Chen 007; Xiu-e Gao; Qingguo Zheng; Jingfeng Wu (CISP-BMEI, 2016)

数据异常的监测技术综述 (Survey on Monitoring Techniques for Data Abnormalities). - Jingfeng Wu; Weidong Jin; Peng Tang (计算机科学, 2017)

The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. - Zhanxing Zhu; Jingfeng Wu; Bing Yu; Lei Wu; Jinwen Ma (CoRR, 2018)

Tangent-Normal Adversarial Regularization for Semi-supervised Learning. - Bing Yu; Jingfeng Wu; Zhanxing Zhu (CoRR, 2018)

Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. - Bing Yu; Jingfeng Wu; Jinwen Ma; Zhanxing Zhu (CVPR, 2019)

Automatic Cloud Segmentation Based on Fused Fully Convolutional Networks. - Jie An 002; Jingfeng Wu; Jinwen Ma (ICIC (1), 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 (ICML, 2019)

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. - Jingfeng Wu; Wenqing Hu; Haoyi Xiong; Jun Huan; Zhanxing Zhu (CoRR, 2019)

Obtaining Adjustable Regularization for Free via Iterate Averaging. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (ICML, 2020)

On the Noisy Gradient Descent that Generalizes as SGD. - Jingfeng Wu; Wenqing Hu; Haoyi Xiong; Jun Huan; Vladimir Braverman; Zhanxing Zhu (ICML, 2020)

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu (CoRR, 2020)

Lifelong Learning with Sketched Structural Regularization. - Haoran Li 006; Aditya Krishnan 001; Jingfeng Wu; Soheil Kolouri; Praveen K. Pilly; Vladimir Braverman (ACML, 2021)

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

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (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 M. Kakade (NeurIPS, 2021)

Ship Compute or Ship Data? Why Not Both? - Jie You; Jingfeng Wu; Xin Jin 008; Mosharaf Chowdhury (NSDI, 2021)

Twenty Years After: Hierarchical Core-Stateless Fair Queueing. - Zhuolong Yu; Jingfeng Wu; Vladimir Braverman; Ion Stoica; Xin Jin 008 (NSDI, 2021)

Programmable packet scheduling with a single queue. - Zhuolong Yu; Chuheng Hu; Jingfeng Wu; Xiao Sun 004; Vladimir Braverman; Mosharaf Chowdhury; Zhenhua Liu 002; Xin Jin 008 (SIGCOMM, 2021)

Gap-Dependent Unsupervised Exploration for Reinforcement Learning. - Jingfeng Wu; Vladimir Braverman; Lin Yang 011 (AISTATS, 2022)

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

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. 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 M. Kakade (NeurIPS, 2022)

Fixed Design Analysis of Regularization-Based Continual Learning. - Haoran Li 006; Jingfeng Wu; Vladimir Braverman (CoLLAs, 2023)

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

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. - Jingfeng Wu; Vladimir Braverman; Jason D. Lee (NeurIPS, 2023)

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

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (CoRR, 2023)

Benign Overfitting of Constant-Stepsize SGD for Linear Regression. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (J. Mach. Learn. Res., 2023)

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

Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. - Xuheng Li; Yihe Deng; Jingfeng Wu; Dongruo Zhou; Quanquan Gu (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 L. Bartlett (ICLR, 2024)

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 001 (ICSOC (1), 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 (NeurIPS, 2024)

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

UELLM: A Unified and Efficient Approach for LLM Inference Serving. - Yiyuan He; Minxian Xu; Jingfeng Wu; Wanyi Zheng; Kejiang Ye; Cheng-Zhong Xu 001 (CoRR, 2024)

Context-Scaling versus Task-Scaling in In-Context Learning. - Amirhesam Abedsoltan; Adityanarayanan Radhakrishnan; Jingfeng Wu; Mikhail Belkin (CoRR, 2024)

A collective AI via lifelong learning and sharing at the edge. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Vladimir Braverman; Eric Eaton; Benjamin Epstein; Yunhao Ge; Lucy Halperin; Jonathan P. How; Laurent Itti; Michael A. Jacobs; Pavan Kantharaju; Long Le; et al. (Nat. Mac. Intell., 2024)

How Does Critical Batch Size Scale in Pre-training? - Hanlin Zhang 002; Depen Morwani; Nikhil Vyas 001; Jingfeng Wu; Difan Zou; Udaya Ghai; Dean P. Foster; Sham M. Kakade (ICLR, 2025)

Cloudnativesim: A Toolkit for Modeling and Simulation of Cloud-Native Applications. - Jingfeng Wu; Minxian Xu; Yiyuan He; Kejiang Ye; Chengzhong Xu 001 (Softw. Pract. Exp., 2025)

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. - Jingfeng Wu; Peter L. Bartlett; Matus Telgarsky; Bin Yu (CoRR, 2025)

Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. - Yuhang Cai; Kangjie Zhou; Jingfeng Wu; Song Mei; Michael Lindsey; Peter L. Bartlett (CoRR, 2025)

Memory-Statistics Tradeoff in Continual Learning with Structural Regularization. - Haoran Li 006; Jingfeng Wu; Vladimir Braverman (CoRR, 2025)

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes. - Ruiqi Zhang; Jingfeng Wu; Licong Lin; Peter L. Bartlett (CoRR, 2025)

Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression. - Jingfeng Wu; Pierre Marion; Peter L. Bartlett (CoRR, 2025)

Improved Scaling Laws in Linear Regression via Data Reuse. - Licong Lin; Jingfeng Wu; Peter L. Bartlett (CoRR, 2025)

A Simplified Analysis of SGD for Linear Regression with Weight Averaging. - Alexandru Meterez; Depen Morwani; Costin-Andrei Oncescu; Jingfeng Wu; Cengiz Pehlevan; Sham M. Kakade (CoRR, 2025)

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling. - Jingfeng Wu; Yiyuan He; Minxian Xu; Xitong Gao; Kejiang Ye; Cheng-Zhong Xu 001 (CoRR, 2025)

Cloud Native System for LLM Inference Serving. - Minxian Xu; Junhan Liao; Jingfeng Wu; Yiyuan He; Kejiang Ye; ChengZhong Xu 001 (CoRR, 2025)