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Jingfeng Wu
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2020 – today
- 2024
- [j2]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:
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [c25]Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. COLT 2024: 5019-5073 - [c24]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR 2024 - [c23]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 - [i23]Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett:
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. CoRR abs/2402.14951 (2024) - [i22]Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu:
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. CoRR abs/2402.15926 (2024) - [i21]Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee:
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR abs/2406.08466 (2024) - [i20]Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett:
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR abs/2406.08654 (2024) - 2023
- [c22]Haoran Li, Jingfeng Wu, Vladimir Braverman:
Fixed Design Analysis of Regularization-Based Continual Learning. CoLLAs 2023: 513-533 - [c21]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML 2023: 37919-37951 - [c20]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS 2023 - [c19]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS 2023 - [i19]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR abs/2303.02255 (2023) - [i18]Haoran Li, Jingfeng Wu, Vladimir Braverman:
Fixed Design Analysis of Regularization-Based Continual Learning. CoRR abs/2303.10263 (2023) - [i17]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR abs/2305.11788 (2023) - [i16]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR abs/2306.09396 (2023) - [i15]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR abs/2310.08391 (2023) - [i14]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR abs/2311.14222 (2023) - 2022
- [c18]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS 2022: 4109-4131 - [c17]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML 2022: 24280-24314 - [c16]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS 2022 - [c15]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS 2022 - [i13]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR abs/2203.03159 (2022) - [i12]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR abs/2208.01857 (2022) - 2021
- [c14]Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman:
Lifelong Learning with Sketched Structural Regularization. ACML 2021: 985-1000 - [c13]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT 2021: 4633-4635 - [c12]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR 2021 - [c11]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS 2021: 5456-5468 - [c10]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS 2021: 13112-13124 - [c9]Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin:
Twenty Years After: Hierarchical Core-Stateless Fair Queueing. NSDI 2021: 29-45 - [c8]Jie You, Jingfeng Wu, Xin Jin, Mosharaf Chowdhury:
Ship Compute or Ship Data? Why Not Both? NSDI 2021: 633-651 - [c7]Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin:
Programmable packet scheduling with a single queue. SIGCOMM 2021: 179-193 - [i11]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR abs/2103.12692 (2021) - [i10]Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman:
Lifelong Learning with Sketched Structural Regularization. CoRR abs/2104.08604 (2021) - [i9]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR abs/2108.04552 (2021) - [i8]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR abs/2108.05439 (2021) - [i7]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR abs/2110.06198 (2021) - 2020
- [c6]Jingfeng Wu, Vladimir Braverman, Lin Yang
:
Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML 2020: 10344-10354 - [c5]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu:
On the Noisy Gradient Descent that Generalizes as SGD. ICML 2020: 10367-10376 - [i6]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR abs/2008.06736 (2020) - [i5]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR abs/2011.02538 (2020) - [i4]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR abs/2011.13034 (2020)
2010 – 2019
- 2019
- [c4]Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu:
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. CVPR 2019: 10676-10684 - [c3]Jie An, Jingfeng Wu, Jinwen Ma:
Automatic Cloud Segmentation Based on Fused Fully Convolutional Networks. ICIC (1) 2019: 520-528 - [c2]Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma:
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. ICML 2019: 7654-7663 - [i3]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu:
The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. CoRR abs/1906.07405 (2019) - 2018
- [i2]Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma:
The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. CoRR abs/1803.00195 (2018) - [i1]Bing Yu, Jingfeng Wu, Zhanxing Zhu:
Tangent-Normal Adversarial Regularization for Semi-supervised Learning. CoRR abs/1808.06088 (2018) - 2017
- [j1]Jingfeng Wu, Weidong Jin, Peng Tang:
数据异常的监测技术综述 (Survey on Monitoring Techniques for Data Abnormalities). 计算机科学 44(Z11): 24-28 (2017) - 2016
- [c1]Bo Chen, Xiu-e Gao, Qingguo Zheng, Jingfeng Wu:
Research on human body composition prediction model based on Akaike Information Criterion and improved entropy method. CISP-BMEI 2016: 1882-1886
Coauthor Index
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last updated on 2024-08-08 20:10 CEST by the dblp team
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