引用次数
引用: 831
H-指数: 15
I10-指数 : 16
出版物: 29
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
international conference on machine learning 7654 -7663
175
2019
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
Zhanxing Zhu , Jingfeng Wu , Bing Yu , Jinwen Ma
arXiv: Learning
29
2018
On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu , Wenqing Hu , Haoyi Xiong , Jun Huan
arXiv: Learning
57
2019
Obtaining Adjustable Regularization for Free via Iterate Averaging
Vladimir Braverman , Lin Yang , Jingfeng Wu
international conference on machine learning 1 10344 -10354
2020
Lifelong Learning with Sketched Structural Regularization.
Vladimir Braverman , Soheil Kolouri , Praveen K. Pilly , Jingfeng Wu
arXiv: Learning
8
2021
The benefits of implicit regularization from sgd in least squares problems
Difan Zou , Jingfeng Wu , Vladimir Braverman , Quanquan Gu
Advances in neural information processing systems 34 5456 -5468
15
2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou , Jingfeng Wu , Vladimir Braverman , Quanquan Gu
COLT
58
2021
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu , Difan Zou , Vladimir Braverman , Quanquan Gu
International Conference on Learning Representations
32
2021
Last iterate risk bounds of sgd with decaying stepsize for overparameterized linear regression
Jingfeng Wu , Difan Zou , Vladimir Braverman , Quanquan Gu
International Conference on Machine Learning 24280 -24314
23
2022
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
Jingfeng Wu , Difan Zou , Zixiang Chen , Vladimir Braverman
arXiv preprint arXiv:2310.08391
17
2023
The power and limitation of pretraining-finetuning for linear regression under covariate shift
Jingfeng Wu , Difan Zou , Vladimir Braverman , Quanquan Gu
Advances in Neural Information Processing Systems 35 33041 -33053
12
2022
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Jingfeng Wu , Difan Zou , Zixiang Chen , Vladimir Braverman
International Conference on Machine Learning
6
2023
Risk bounds of multi-pass sgd for least squares in the interpolation regime
Difan Zou , Jingfeng Wu , Vladimir Braverman , Quanquan Gu
Advances in Neural Information Processing Systems 35 12909 -12920
4
2022
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression
Xuheng Li , Yihe Deng , Jingfeng Wu , Dongruo Zhou
arXiv preprint arXiv:2311.14222
2023
A collective AI via lifelong learning and sharing at the edge
Andrea Soltoggio , Eseoghene Ben-Iwhiwhu , Vladimir Braverman , Eric Eaton
Nature Machine Intelligence 6 ( 3) 251 -264
1
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
arXiv preprint arXiv:2402.15926
1
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
arXiv preprint arXiv:2402.14951
1
2024
Implicit bias of gradient descent for logistic regression at the edge of stability
Jingfeng Wu , Vladimir Braverman , Jason D Lee
Advances in Neural Information Processing Systems 36
8
2024
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Jingfeng Wu , Wennan Zhu , Peter Kairouz , Vladimir Braverman
Advances in Neural Information Processing Systems 36
2024