Jingfeng Wu
Title
Cited by
Cited by
Year
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects.
Z Zhu, J Wu, B Yu, L Wu, J Ma
International Conference on Machine Learning, 7654-7663, 2019
1082019
Tangent-normal adversarial regularization for semi-supervised learning
B Yu, J Wu, J Ma, Z Zhu
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
212019
On the Noisy Gradient Descent that Generalizes as SGD
J Wu, W Hu, H Xiong, J Huan, V Braverman, Z Zhu
International Conference on Machine Learning, 10367-10376, 2020
18*2020
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
J Wu, D Zou, V Braverman, Q Gu
arXiv preprint arXiv:2011.02538, 2020
82020
Benign overfitting of constant-stepsize sgd for linear regression
D Zou, J Wu, V Braverman, Q Gu, SM Kakade
arXiv preprint arXiv:2103.12692, 2021
62021
Twenty Years After: Hierarchical Core-Stateless Fair Queueing.
Z Yu, J Wu, V Braverman, I Stoica, X Jin
NSDI, 29-45, 2021
32021
The benefits of implicit regularization from sgd in least squares problems
D Zou, J Wu, V Braverman, Q Gu, DP Foster, SM Kakade
arXiv preprint arXiv:2108.04552, 2021
22021
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
J Wu, V Braverman, LF Yang
arXiv preprint arXiv:2011.13034, 2020
22020
Programmable packet scheduling with a single queue
Z Yu, C Hu, J Wu, X Sun, V Braverman, M Chowdhury, Z Liu, X Jin
Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 179-193, 2021
12021
Lifelong Learning with Sketched Structural Regularization
H Li, A Krishnan, J Wu, S Kolouri, PK Pilly, V Braverman
arXiv preprint arXiv:2104.08604, 2021
12021
Ship Compute or Ship Data? Why Not Both?
J You, J Wu, X Jin, M Chowdhury
NSDI, 633-651, 2021
12021
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
J Wu, D Zou, V Braverman, Q Gu, SM Kakade
arXiv preprint arXiv:2110.06198, 2021
2021
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
J Wu, V Braverman, LF Yang
arXiv preprint arXiv:2108.05439, 2021
2021
Obtaining Adjustable Regularization for Free via Iterate Averaging
J Wu, V Braverman, L Yang
International Conference on Machine Learning, 10344-10354, 2020
2020
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Articles 1–14