| Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks S Arora, S Du, W Hu, Z Li, R Wang International conference on machine learning, 322-332, 2019 | 1297 | 2019 |
| On exact computation with an infinitely wide neural net S Arora, SS Du, W Hu, Z Li, RR Salakhutdinov, R Wang Advances in neural information processing systems 32, 2019 | 1242 | 2019 |
| Implicit regularization in deep matrix factorization S Arora, N Cohen, W Hu, Y Luo Advances in neural information processing systems 32, 2019 | 724 | 2019 |
| A convergence analysis of gradient descent for deep linear neural networks S Arora, N Cohen, N Golowich, W Hu arXiv preprint arXiv:1810.02281, 2018 | 370 | 2018 |
| Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020 | 350 | 2020 |
| An analysis of the t-sne algorithm for data visualization S Arora, W Hu, PK Kothari Conference on learning theory, 1455-1462, 2018 | 319 | 2018 |
| Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced SS Du, W Hu, JD Lee Advances in neural information processing systems 31, 2018 | 308 | 2018 |
| Combinatorial multi-armed bandit with general reward functions W Chen, W Hu, F Li, J Li, Y Liu, P Lu Advances in Neural Information Processing Systems 29, 2016 | 188 | 2016 |
| Provable benefit of orthogonal initialization in optimizing deep linear networks W Hu, L Xiao, J Pennington arXiv preprint arXiv:2001.05992, 2020 | 176 | 2020 |
| Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity SS Du, W Hu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 158 | 2019 |
| Enhanced convolutional neural tangent kernels Z Li, R Wang, D Yu, SS Du, W Hu, R Salakhutdinov, S Arora arXiv preprint arXiv:1911.00809, 2019 | 155 | 2019 |
| Explaining landscape connectivity of low-cost solutions for multilayer nets R Kuditipudi, X Wang, H Lee, Y Zhang, Z Li, W Hu, R Ge, S Arora Advances in neural information processing systems 32, 2019 | 116 | 2019 |
| Width provably matters in optimization for deep linear neural networks S Du, W Hu International Conference on Machine Learning, 1655-1664, 2019 | 115 | 2019 |
| More than a toy: Random matrix models predict how real-world neural representations generalize A Wei, W Hu, J Steinhardt International conference on machine learning, 23549-23588, 2022 | 112 | 2022 |
| Simple and effective regularization methods for training on noisily labeled data with generalization guarantee W Hu, Z Li, D Yu arXiv preprint arXiv:1905.11368, 2019 | 104 | 2019 |
| Impact of representation learning in linear bandits J Yang, W Hu, JD Lee, SS Du arXiv preprint arXiv:2010.06531, 2020 | 90* | 2020 |
| The surprising simplicity of the early-time learning dynamics of neural networks W Hu, L Xiao, B Adlam, J Pennington Advances in Neural Information Processing Systems 33, 17116-17128, 2020 | 90 | 2020 |
| How do transformers learn in-context beyond simple functions? a case study on learning with representations T Guo, W Hu, S Mei, H Wang, C Xiong, S Savarese, Y Bai arXiv preprint arXiv:2310.10616, 2023 | 88 | 2023 |
| Implicit bias in leaky relu networks trained on high-dimensional data S Frei, G Vardi, PL Bartlett, N Srebro, W Hu arXiv preprint arXiv:2210.07082, 2022 | 74 | 2022 |
| Linear convergence of a frank-wolfe type algorithm over trace-norm balls Z Allen-Zhu, E Hazan, W Hu, Y Li Advances in neural information processing systems 30, 2017 | 73 | 2017 |
Simon Shaolei DuAssociate Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
