Jingfeng Wu - RankMe

78 papers A* 25A 1C 1Misc 2Journal 45Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BanaServe: Unified KV Cache and Dynamic Module Migration for Balancing Disaggregated LLM Serving in AI Infrastructure. Softw. Pract. Exp. Yiyuan He, Minxian Xu, Jingfeng Wu, Jianmin Hu, Chong Ma, Min Shen, Le Chen, Cheng-Zhong Xu, Lin Qu, Kejiang Ye
2025	J	`jnl`	A Simplified Analysis of SGD for Linear Regression with Weight Averaging. CoRR Alexandru Meterez, Depen Morwani, Costin-Andrei Oncescu, Jingfeng Wu, Cengiz Pehlevan, Sham M. Kakade
2025	J	`jnl`	BanaServe: Unified KV Cache and Dynamic Module Migration for Balancing Disaggregated LLM Serving in AI Infrastructure. CoRR Yiyuan He, Minxian Xu, Jingfeng Wu, Jianmin Hu, Chong Ma, Min Shen, Le Chen, Cheng-Zhong Xu, Lin Qu, Kejiang Ye
2025	A*	`conf`	Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. ICML Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
2025	J	`jnl`	Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. CoRR Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
2025	J	`jnl`	Cloud Native System for LLM Inference Serving. CoRR Minxian Xu, Junhan Liao, Jingfeng Wu, Yiyuan He, Kejiang Ye, Chengzhong Xu
2025	J	`jnl`	Cloudnativesim: A Toolkit for Modeling and Simulation of Cloud-Native Applications. Softw. Pract. Exp. Jingfeng Wu, Minxian Xu, Yiyuan He, Kejiang Ye, Chengzhong Xu
2025	A*	`conf`	Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes. ICML Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett
2025	A*	`conf`	How Does Critical Batch Size Scale in Pre-training? ICLR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2025	A*	`conf`	Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. ICML Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett
2025	J	`jnl`	Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. CoRR Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett
2025	J	`jnl`	Improved Scaling Laws in Linear Regression via Data Reuse. CoRR Licong Lin, Jingfeng Wu, Peter L. Bartlett
2025	J	`jnl`	Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression. CoRR Jingfeng Wu, Pierre Marion, Peter L. Bartlett
2025	J	`jnl`	Memory-Statistics Tradeoff in Continual Learning with Structural Regularization. CoRR Haoran Li, Jingfeng Wu, Vladimir Braverman
2025	J	`jnl`	Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes. CoRR Ruiqi Zhang, Jingfeng Wu, Licong Lin, Peter L. Bartlett
2025	J	`jnl`	On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models. CoRR Yi Zhang, Zhenyu Liao, Jingfeng Wu, Difan Zou
2025	J	`jnl`	Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization. CoRR Jingfeng Wu, Peter L. Bartlett, Jason D. Lee, Sham M. Kakade, Bin Yu
2025	J	`jnl`	Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling. CoRR Alexandru Meterez, Depen Morwani, Jingfeng Wu, Costin-Andrei Oncescu, Cengiz Pehlevan, Sham M. Kakade
2025	J	`jnl`	Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling. CoRR Jingfeng Wu, Yiyuan He, Minxian Xu, Xitong Gao, Kejiang Ye, Cheng-Zhong Xu
2024	J	`jnl`	A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 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
2024	J	`jnl`	CloudNativeSim: a toolkit for modeling and simulation of cloud-native applications. CoRR Jingfeng Wu, Minxian Xu, Yiyuan He, Kejiang Ye, Chengzhong Xu
2024	J	`jnl`	Context-Scaling versus Task-Scaling in In-Context Learning. CoRR Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu, Mikhail Belkin
2024	J	`jnl`	How Does Critical Batch Size Scale in Pre-training? CoRR Hanlin Zhang, Depen Morwani, Nikhil Vyas, Jingfeng Wu, Difan Zou, Udaya Ghai, Dean P. Foster, Sham M. Kakade
2024	A*	`conf`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2024	A*	`conf`	In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. NeurIPS Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett
2024	J	`jnl`	In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. CoRR Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett
2024	A*	`conf`	Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
2024	J	`jnl`	Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu
2024	A*	`conf`	Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. NeurIPS Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett
2024	J	`jnl`	Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett
2024	A*	`conf`	Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2024	A*	`conf`	Scaling Laws in Linear Regression: Compute, Parameters, and Data. NeurIPS Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
2024	J	`jnl`	Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
2024	J	`jnl`	UELLM: A Unified and Efficient Approach for LLM Inference Serving. CoRR Yiyuan He, Minxian Xu, Jingfeng Wu, Wanyi Zheng, Kejiang Ye, Cheng-Zhong Xu
2024	—	`conf`	UELLM: A Unified and Efficient Approach for Large Language Model Inference Serving. ICSOC (1) Yiyuan He, Minxian Xu, Jingfeng Wu, Wanyi Zheng, Kejiang Ye, Cheng-Zhong Xu
2023	J	`jnl`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. J. Mach. Learn. Res. Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	A*	`conf`	Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	—	`conf`	Fixed Design Analysis of Regularization-Based Continual Learning. CoLLAs Haoran Li, Jingfeng Wu, Vladimir Braverman
2023	J	`jnl`	Fixed Design Analysis of Regularization-Based Continual Learning. CoRR Haoran Li, Jingfeng Wu, Vladimir Braverman
2023	J	`jnl`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2023	A*	`conf`	Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS Jingfeng Wu, Vladimir Braverman, Jason D. Lee
2023	J	`jnl`	Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR Jingfeng Wu, Vladimir Braverman, Jason D. Lee
2023	J	`jnl`	Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	A*	`conf`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2022	A	`conf`	Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS Jingfeng Wu, Vladimir Braverman, Lin Yang
2022	A*	`conf`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A*	`conf`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A*	`conf`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	A*	`conf`	Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS Jingfeng Wu, Vladimir Braverman, Lin Yang
2021	A*	`conf`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	J	`jnl`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	A*	`conf`	Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2021	J	`jnl`	Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2021	J	`jnl`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	C	`conf`	Lifelong Learning with Sketched Structural Regularization. ACML Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
2021	J	`jnl`	Lifelong Learning with Sketched Structural Regularization. CoRR Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
2021	A*	`conf`	Programmable packet scheduling with a single queue. SIGCOMM Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin
2021	Misc	`conf`	Ship Compute or Ship Data? Why Not Both? NSDI Jie You, Jingfeng Wu, Xin Jin, Mosharaf Chowdhury
2021	A*	`conf`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021	J	`jnl`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021	Misc	`conf`	Twenty Years After: Hierarchical Core-Stateless Fair Queueing. NSDI Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin
2020	J	`jnl`	Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2020	J	`jnl`	Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2020	A*	`conf`	Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML Jingfeng Wu, Vladimir Braverman, Lin Yang
2020	J	`jnl`	Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2020	A*	`conf`	On the Noisy Gradient Descent that Generalizes as SGD. ICML Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
2019	—	`conf`	Automatic Cloud Segmentation Based on Fused Fully Convolutional Networks. ICIC (1) Jie An, Jingfeng Wu, Jinwen Ma
2019	A*	`conf`	Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. CVPR Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu
2019	A*	`conf`	The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. ICML Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma
2019	J	`jnl`	The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. CoRR Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu
2018	J	`jnl`	Tangent-Normal Adversarial Regularization for Semi-supervised Learning. CoRR Bing Yu, Jingfeng Wu, Zhanxing Zhu
2018	J	`jnl`	The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. CoRR Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma
2017	J	`jnl`	数据异常的监测技术综述 (Survey on Monitoring Techniques for Data Abnormalities). 计算机科学 Jingfeng Wu, Weidong Jin, Peng Tang
2016	—	`conf`	Research on human body composition prediction model based on Akaike Information Criterion and improved entropy method. CISP-BMEI Bo Chen, Xiu-e Gao, Qingguo Zheng, Jingfeng Wu