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
Kaifeng LyuPrinceton UniversityVerified email at princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.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. FosterPrincipal Researcher, Microsoft ResearchVerified email at microsoft.com
Thodoris LykourisMITVerified email at mit.edu
Yuping LuoComputer Science Department, Princeton UniversityVerified email at cs.princeton.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

Zhiyuan Li

Assistant Professor, Toyota Technological Institute at Chicago

Verified email at ttic.edu - Homepage

deep learning theory machine learning theory


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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	835	2019
On Exact Computation with an Infinitely Wide Neural Net. S Arora, SS Du, W Hu, Z Li, R Salakhutdinov, R Wang arXiv preprint arXiv:1904.11955, 2019	771	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	526	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	159	2019
An exponential learning rate schedule for deep learning Z Li, S Arora arXiv preprint arXiv:1910.07454, 2019	147	2019
Theoretical analysis of auto rate-tuning by batch normalization S Arora, Z Li, K Lyu arXiv preprint arXiv:1812.03981, 2018	110	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	109	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	107	2016
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	75	2020
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	71	2019
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	67*	2019
What Happens after SGD Reaches Zero Loss?--A Mathematical Framework Z Li, T Wang, S Arora arXiv preprint arXiv:2110.06914, 2021	48	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	44	2021
Risk bounds and rademacher complexity in batch reinforcement learning Y Duan, C Jin, Z Li International Conference on Machine Learning, 2892-2902, 2021	44	2021
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	44	2020
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	39	2022
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	39	2021
Why are convolutional nets more sample-efficient than fully-connected nets? Z Li, Y Zhang, S Arora arXiv preprint arXiv:2010.08515, 2020	39	2020
Understanding the generalization benefit of normalization layers: Sharpness reduction K Lyu, Z Li, S Arora Advances in Neural Information Processing Systems 35, 34689-34708, 2022	33	2022
How Does Sharpness-Aware Minimization Minimizes Sharpness? K Wen, T Ma, Z Li The Eleventh International Conference on Learning Representations, 2023	23*	2023

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