Zhiyuan Li

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Soavtorji

Sanjeev AroraProfessor of Computer Science, Princeton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganPreverjeni e-poštni naslov na umich.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPreverjeni e-poštni naslov na cs.washington.edu
Ruosong WangAssistant Professor, Peking UniversityPreverjeni e-poštni naslov na pku.edu.cn
Kaifeng LyuPrinceton UniversityPreverjeni e-poštni naslov na princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPreverjeni e-poštni naslov na cs.cmu.edu
Dingli YuPrinceton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Srinadh BhojanapalliResearch Scientist, Google ResearchPreverjeni e-poštni naslov na google.com
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityPreverjeni e-poštni naslov na cs.nyu.edu
Behnam NeyshaburSenior Staff Research Scientist, DeepMindPreverjeni e-poštni naslov na google.com
Nathan SrebroProfessor, TTIC and University of ChicagoPreverjeni e-poštni naslov na ttic.edu
Yi ZhangSenior Researcher at Microsoft Research RedmondPreverjeni e-poštni naslov na microsoft.com
Yuping LuoComputer Science Department, Princeton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Eva TardosProfessor of Computer Science, Cornell UniversityPreverjeni e-poštni naslov na cornell.edu
Karthik SridharanCornell University, University of Pennsylvania, Toyota Technological InstitutePreverjeni e-poštni naslov na ttic.edu
Dylan J. FosterPrincipal Researcher, Microsoft ResearchPreverjeni e-poštni naslov na microsoft.com
Thodoris LykourisMITPreverjeni e-poštni naslov na mit.edu
Rong GeDuke UniversityPreverjeni e-poštni naslov na cs.duke.edu
Holden LeeAssistant Professor of Applied Mathematics and Statistics, Johns Hopkins UniversityPreverjeni e-poštni naslov na jhu.edu
Xiang WangMeta, Duke UniversityPreverjeni e-poštni naslov na meta.com

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Zhiyuan Li

Assistant Professor, Toyota Technological Institute at Chicago

Preverjeni e-poštni naslov na ttic.edu - Domača stran

deep learning theory machine learning theory


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	1039	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	982	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	604	2018
An exponential learning rate schedule for deep learning Z Li, S Arora arXiv preprint arXiv:1910.07454, 2019	212	2019
Harnessing the power of infinitely wide deep nets on small-data tasks S Arora, SS Du, Z Li, R Salakhutdinov, R Wang, D Yu	188	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	134	2020
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	128	2019
Theoretical analysis of auto rate-tuning by batch normalization S Arora, Z Li, K Lyu arXiv preprint arXiv:1812.03981, 2018	128	2018
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	124	2016
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	101	2022
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	95*	2019
What Happens after SGD Reaches Zero Loss?--A Mathematical Framework Z Li, T Wang, S Arora arXiv preprint arXiv:2110.06914, 2021	94	2021
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	91	2019
Sophia: A scalable stochastic second-order optimizer for language model pre-training H Liu, Z Li, D Hall, P Liang, T Ma arXiv preprint arXiv:2305.14342, 2023	88	2023
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	78	2021
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	72	2021
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	69	2022
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	68	2020
How Does Sharpness-Aware Minimization Minimizes Sharpness? K Wen, T Ma, Z Li The Eleventh International Conference on Learning Representations, 2023	61	2023
Why are convolutional nets more sample-efficient than fully-connected nets? Z Li, Y Zhang, S Arora arXiv preprint arXiv:2010.08515, 2020	59	2020

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