Jingfeng Wu

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Vladimir BravermanVictor E. Cameron Professor of Computer Science, Rice UniversityPatvirtintas el. paštas rice.edu
Difan ZouThe University of Hong KongPatvirtintas el. paštas cs.hku.hk
Quanquan GuAssociate Professor of Computer Science, UCLAPatvirtintas el. paštas cs.ucla.edu
Zhanxing ZhuAssociate Professor, ECS, University of Southampton; Changping National LabPatvirtintas el. paštas soton.ac.uk
Sham M KakadeHarvard UniversityPatvirtintas el. paštas seas.harvard.edu
Bing YuXiaohongshuPatvirtintas el. paštas pku.edu.cn
Xin JinPeking UniversityPatvirtintas el. paštas pku.edu.cn
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyPatvirtintas el. paštas cs.berkeley.edu
Zhuolong YuMicrosoftPatvirtintas el. paštas microsoft.com
Haoyi XiongMicrosoftPatvirtintas el. paštas microsoft.com
Jun (Luke) HuanAWS AI LabsPatvirtintas el. paštas amazon.com
Wenqing HuMissouri University of Science and Technology (formerly University of Missouri, Rolla)Patvirtintas el. paštas mst.edu
Mosharaf ChowdhuryUniversity of MichiganPatvirtintas el. paštas umich.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAPatvirtintas el. paštas ee.ucla.edu
Zixiang ChenGraduate Student of CS, UCLAPatvirtintas el. paštas cs.ucla.edu
Zhenhua LiuAssociate Professor at Stony Brook UniversityPatvirtintas el. paštas stonybrook.edu
Haoran LiRice UniversityPatvirtintas el. paštas rice.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Ion StoicaProfessor of Computer Science, UC BerkeleyPatvirtintas el. paštas cs.berkeley.edu
Dean FosterAmazon, NYCPatvirtintas el. paštas amazon.com

Stebėti

Jingfeng Wu

University of California, Berkeley

Patvirtintas el. paštas berkeley.edu - Pagrindinis puslapis

deep learning theory algorithms machine learning optimization statistical learning theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	276*	2019
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	113	2020
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	85	2021
Benign overfitting of constant-stepsize SGD for linear regression D Zou, J Wu, V Braverman, Q Gu, SM Kakade Journal of Machine Learning Research 24 (326), 1-58, 2023	62	2023
Direction matters: On the implicit bias of stochastic gradient descent with moderate learning rate J Wu, D Zou, V Braverman, Q Gu International Conference on Learning Representations, 2021	37	2021
Twenty years after: Hierarchical {Core-Stateless} fair queueing Z Yu, J Wu, V Braverman, I Stoica, X Jin 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2021	37	2021
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	37	2019
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? J Wu, D Zou, Z Chen, V Braverman, Q Gu, PL Bartlett arXiv preprint arXiv:2310.08391, 2023	34	2023
The benefits of implicit regularization from sgd in least squares problems D Zou, J Wu, V Braverman, Q Gu, DP Foster, S Kakade Advances in neural information processing systems 34, 5456-5468, 2021	30	2021
Last iterate risk bounds of sgd with decaying stepsize for overparameterized linear regression J Wu, D Zou, V Braverman, Q Gu, S Kakade International Conference on Machine Learning, 24280-24314, 2022	24	2022
Ship compute or ship data? why not both? J You, J Wu, X Jin, M Chowdhury 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2021	22	2021
The power and limitation of pretraining-finetuning for linear regression under covariate shift J Wu, D Zou, V Braverman, Q Gu, S Kakade Advances in Neural Information Processing Systems 35, 33041-33053, 2022	18	2022
Lifelong learning with sketched structural regularization H Li, A Krishnan, J Wu, S Kolouri, PK Pilly, V Braverman Asian conference on machine learning, 985-1000, 2021	17	2021
Gap-dependent unsupervised exploration for reinforcement learning J Wu, V Braverman, L Yang International Conference on Artificial Intelligence and Statistics, 4109-4131, 2022	15	2022
Accommodating picky customers: Regret bound and exploration complexity for multi-objective reinforcement learning J Wu, V Braverman, L Yang Advances in Neural Information Processing Systems 34, 13112-13124, 2021	15	2021
Implicit bias of gradient descent for logistic regression at the edge of stability J Wu, V Braverman, JD Lee Advances in Neural Information Processing Systems 36, 2024	13	2024
Fixed design analysis of regularization-based continual learning H Li, J Wu, V Braverman Conference on Lifelong Learning Agents, 513-533, 2023	7	2023
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron J Wu, D Zou, Z Chen, V Braverman, Q Gu, SM Kakade International Conference on Machine Learning, 2023	6*	2023
Risk bounds of multi-pass sgd for least squares in the interpolation regime D Zou, J Wu, V Braverman, Q Gu, S Kakade Advances in Neural Information Processing Systems 35, 12909-12920, 2022	6	2022
Obtaining Adjustable Regularization for Free via Iterate Averaging J Wu, V Braverman, L Yang International Conference on Machine Learning, 10344-10354, 2020	5	2020

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