| 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 | 293* | 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 | 123 | 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 | 97 | 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 | 74 | 2023 |
| 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 | 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 | 44 | 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 | 43 | 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 | 38 | 2019 |
| 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 | 34 | 2021 |
| 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 | 28 | 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 | 25 | 2022 |
| 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 | 20 | 2024 |
| A collective AI via lifelong learning and sharing at the edge A Soltoggio, E Ben-Iwhiwhu, V Braverman, E Eaton, B Epstein, Y Ge, ... Nature Machine Intelligence 6 (3), 251-264, 2024 | 19 | 2024 |
| 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 | 19 | 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 | 18 | 2021 |
| Gap-dependent unsupervised exploration for reinforcement learning J Wu, V Braverman, L Yang International Conference on Artificial Intelligence and Statistics, 4109-4131, 2022 | 17 | 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 | 16 | 2021 |
| In-context learning of a linear Transformer block: benefits of the MLP component and one-step GD initialization R Zhang, J Wu, PL Bartlett arXiv preprint arXiv:2402.14951, 2024 | 14 | 2024 |
| Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency J Wu, PL Bartlett, M Telgarsky, B Yu arXiv preprint arXiv:2402.15926, 2024 | 11 | 2024 |
| Fixed design analysis of regularization-based continual learning H Li, J Wu, V Braverman Conference on Lifelong Learning Agents, 513-533, 2023 | 11 | 2023 |
Vladimir BravermanProfessor of Computer Science, Johns Hopkins University; Google Research; Adjunct Professor, Rice U.在 cs.jhu.edu 的電子郵件地址已通過驗證