Zhiyuan Li

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Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganVerified email at umich.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Ruosong WangPhD Student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Kaifeng LyuPrinceton UniversityVerified email at princeton.edu
Dingli YuPrinceton UniversityVerified email at cs.princeton.edu
Srinadh BhojanapalliResearch Scientist, Google ResearchVerified email at google.com
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Behnam NeyshaburSenior Staff Research Scientist, DeepMindVerified email at google.com
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Yi ZhangSenior Researcher at Microsoft Research RedmondVerified email at microsoft.com
Eva TardosProfessor of Computer Science, Cornell UniversityVerified email at cornell.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstituteVerified email at ttic.edu
Dylan J. FosterMicrosoft ResearchVerified email at microsoft.com
Thodoris LykourisMITVerified email at mit.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Holden LeeAssistant Professor of Applied Mathematics and Statistics, Johns Hopkins UniversityVerified email at jhu.edu
Xiang WangMeta, Duke UniversityVerified email at meta.com
Rohith KuditipudiStanford UniversityVerified email at stanford.edu

Zhiyuan Li

Postdoc, Stanford University

Verified email at stanford.edu - Homepage

deep learning theory machine learning theory


Title Sort by citations Sort by year Sort by 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	778	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	698	2019
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	493	2018
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	138	2019
An exponential learning rate schedule for deep learning Z Li, S Arora arXiv preprint arXiv:1910.07454, 2019	131	2019
Theoretical analysis of auto rate-tuning by batch normalization S Arora, Z Li, K Lyu arXiv preprint arXiv:1812.03981, 2018	103	2018
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	100	2019
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	100	2016
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	66	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	62	2020
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee W Hu, Z Li, D Yu International Conference on Learning Representations (ICLR 2020), 2019	53*	2019
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	41	2020
What Happens after SGD Reaches Zero Loss?--A Mathematical Framework Z Li, T Wang, S Arora arXiv preprint arXiv:2110.06914, 2021	39	2021
Risk bounds and rademacher complexity in batch reinforcement learning Y Duan, C Jin, Z Li International Conference on Machine Learning, 2892-2902, 2021	39	2021
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	38	2021
Why are convolutional nets more sample-efficient than fully-connected nets? Z Li, Y Zhang, S Arora arXiv preprint arXiv:2010.08515, 2020	36	2020
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	35	2021
Understanding gradient descent on the edge of stability in deep learning S Arora, Z Li, A Panigrahi International Conference on Machine Learning, 948-1024, 2022	29	2022
Understanding the generalization benefit of normalization layers: Sharpness reduction K Lyu, Z Li, S Arora arXiv preprint arXiv:2206.07085, 2022	22	2022
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	21	2016

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