Jingfeng Wu

According to our database, Jingfeng Wu authored at least 49 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Cloud Native System for LLM Inference Serving.

Minxian Xu, Junhan Liao, Jingfeng Wu, Yiyuan He, Kejiang Ye, ChengZhong Xu

CoRR, July, 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

CoRR, July, 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, June, 2025

Improved Scaling Laws in Linear Regression via Data Reuse.

Licong Lin, Jingfeng Wu, Peter L. Bartlett

CoRR, June, 2025

Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression.

Jingfeng Wu, Pierre Marion, Peter L. Bartlett

CoRR, June, 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, April, 2025

Memory-Statistics Tradeoff in Continual Learning with Structural Regularization.

Haoran Li, Jingfeng Wu, Vladimir Braverman

CoRR, April, 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, February, 2025

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression.

Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

CoRR, February, 2025

Cloudnativesim: A Toolkit for Modeling and Simulation of Cloud-Native Applications.

Jingfeng Wu, Minxian Xu, Yiyuan He, Kejiang Ye, Chengzhong Xu

Softw. Pract. Exp., 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy, Shahaf S. Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, Soheil Kolouri

Nat. Mac. Intell., 2024

Context-Scaling versus Task-Scaling in In-Context Learning.

Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu, Mikhail Belkin

CoRR, 2024

UELLM: A Unified and Efficient Approach for LLM Inference Serving.

Yiyuan He, Minxian Xu, Jingfeng Wu, Wanyi Zheng, Kejiang Ye, Cheng-Zhong Xu

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Scaling Laws in Linear Regression: Compute, Parameters, and Data.

Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Service-Oriented Computing - 22nd International Conference, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Risk Bounds of Accelerated SGD for Overparameterized Linear Regression.

Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

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

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

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance.

Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability.

Jingfeng Wu, Vladimir Braverman, Jason D. Lee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron.

Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

Proceedings of the International Conference on Machine Learning, 2023

Fixed Design Analysis of Regularization-Based Continual Learning.

Haoran Li, Jingfeng Wu, Vladimir Braverman

Proceedings of the Conference on Lifelong Learning Agents, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

Gap-Dependent Unsupervised Exploration for Reinforcement Learning.

Jingfeng Wu, Vladimir Braverman, Lin Yang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Programmable packet scheduling with a single queue.

Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin

Proceedings of the ACM SIGCOMM 2021 Conference, Virtual Event, USA, August 23-27, 2021., 2021

Twenty Years After: Hierarchical Core-Stateless Fair Queueing.

Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 2021

Ship Compute or Ship Data? Why Not Both?

Jie You, Jingfeng Wu, Xin Jin, Mosharaf Chowdhury

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning.

Jingfeng Wu, Vladimir Braverman, Lin Yang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate.

Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

Proceedings of the 9th International Conference on Learning Representations, 2021

Lifelong Learning with Sketched Structural Regularization.

Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman

Proceedings of the Asian Conference on Machine Learning, 2021

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

On the Noisy Gradient Descent that Generalizes as SGD.

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

Proceedings of the 37th International Conference on Machine Learning, 2020

Obtaining Adjustable Regularization for Free via Iterate Averaging.

Jingfeng Wu, Vladimir Braverman, Lin Yang

Proceedings of the 37th International Conference on Machine Learning, 2020

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

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

Proceedings of the 36th International Conference on Machine Learning, 2019

Automatic Cloud Segmentation Based on Fused Fully Convolutional Networks.

Jie An, Jingfeng Wu, Jinwen Ma

Proceedings of the Intelligent Computing Theories and Application, 2019

Tangent-Normal Adversarial Regularization for Semi-Supervised Learning.

Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Tangent-Normal Adversarial Regularization for Semi-supervised Learning.

Bing Yu, Jingfeng Wu, Zhanxing Zhu

CoRR, 2018

The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent.

Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

CoRR, 2018

2017

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

2016

Research on human body composition prediction model based on Akaike Information Criterion and improved entropy method.

Bo Chen, Xiu-e Gao, Qingguo Zheng, Jingfeng Wu

Proceedings of the 9th International Congress on Image and Signal Processing, 2016