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Zhiyuan Li
Zhiyuan Li
Graduate Student, Princeton University
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
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
6362019
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
5642019
Towards understanding the role of over-parametrization in generalization of neural networks
B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro
arXiv preprint arXiv:1805.12076, 2018
4092018
Harnessing the power of infinitely wide deep nets on small-data tasks
S Arora, SS Du, Z Li, R Salakhutdinov, R Wang, D Yu
arXiv preprint arXiv:1910.01663, 2019
1132019
An exponential learning rate schedule for deep learning
Z Li, S Arora
arXiv preprint arXiv:1910.07454, 2019
1062019
Learning in games: Robustness of fast convergence
DJ Foster, Z Li, T Lykouris, K Sridharan, E Tardos
Advances in Neural Information Processing Systems 29, 2016
862016
Theoretical analysis of auto rate-tuning by batch normalization
S Arora, Z Li, K Lyu
arXiv preprint arXiv:1812.03981, 2018
842018
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
752019
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
482019
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
38*2019
Towards resolving the implicit bias of gradient descent for matrix factorization: Greedy low-rank learning
Z Li, Y Luo, K Lyu
arXiv preprint arXiv:2012.09839, 2020
372020
Why are convolutional nets more sample-efficient than fully-connected nets?
Z Li, Y Zhang, S Arora
arXiv preprint arXiv:2010.08515, 2020
302020
Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate
Z Li, K Lyu, S Arora
Advances in Neural Information Processing Systems 33, 14544-14555, 2020
272020
On the validity of modeling sgd with stochastic differential equations (sdes)
Z Li, S Malladi, S Arora
Advances in Neural Information Processing Systems 34, 12712-12725, 2021
212021
Risk bounds and rademacher complexity in batch reinforcement learning
Y Duan, C Jin, Z Li
International Conference on Machine Learning, 2892-2902, 2021
212021
Solving marginal map problems with np oracles and parity constraints
Y Xue, Z Li, S Ermon, CP Gomes, B Selman
Advances in Neural Information Processing Systems 29, 2016
182016
Online Improper Learning with an Approximation Oracle
E Hazan, W Hu, Y Li, Z Li
Advances in Neural Information Processing Systems, 5657-5665, 2018
152018
Gradient descent on two-layer nets: Margin maximization and simplicity bias
K Lyu, Z Li, R Wang, S Arora
Advances in Neural Information Processing Systems 34, 12978-12991, 2021
132021
Implicit regularization and convergence for weight normalization
X Wu, E Dobriban, T Ren, S Wu, Z Li, S Gunasekar, R Ward, Q Liu
Advances in Neural Information Processing Systems 33, 2835-2847, 2020
12*2020
What Happens after SGD Reaches Zero Loss?--A Mathematical Framework
Z Li, T Wang, S Arora
arXiv preprint arXiv:2110.06914, 2021
112021
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