Zeyuan Allen-Zhu

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Weizhu ChenMicrosoftPatvirtintas el. paštas microsoft.com
Yuanzhi LiAssistant Professor at CMUPatvirtintas el. paštas andrew.cmu.edu
Lorenzo OrecchiaUniversity of Chicago, Computer SciencePatvirtintas el. paštas bu.edu
Phillip WallisGooglePatvirtintas el. paštas google.com
Yelong ShenMicrosoftPatvirtintas el. paštas microsoft.com
Edward HuOpenAIPatvirtintas el. paštas openai.com
Elad HazanProfessor at Princeton University and Director Google AI PrincetonPatvirtintas el. paštas princeton.edu
Yang YuanTsinghua UniversityPatvirtintas el. paštas tsinghua.edu.cn
Zhao SongSimons Institute for the Theory of Computing, UC BerkeleyPatvirtintas el. paštas ias.edu
Alessandro ChiesaEPFLPatvirtintas el. paštas epfl.ch
Sebastien BubeckOpenAIPatvirtintas el. paštas openai.com
Chenguang ZhuZoomPatvirtintas el. paštas zoom.us
Naman AgarwalSenior Research Scientist, Google AI PrincetonPatvirtintas el. paštas google.com
Tengyu MAStanford UniversityPatvirtintas el. paštas stanford.edu
Brian BullinsAssistant Professor, Purdue UniversityPatvirtintas el. paštas purdue.edu
Zhenyu LiaoApplied Scientist in Amazon Inc.Patvirtintas el. paštas amazon.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPatvirtintas el. paštas cs.berkeley.edu
Chi JinAssistant Professor, Princeton UniversityPatvirtintas el. paštas princeton.edu
Pinyan LuITCS, Shanghai University of Finance and EconomicsPatvirtintas el. paštas mail.shufe.edu.cn
Xiaorui SunUniversity of Illinois at ChicagoPatvirtintas el. paštas uic.edu

Stebėti

Zeyuan Allen-Zhu

Meta AI / FAIR Labs

Patvirtintas el. paštas csail.mit.edu - Pagrindinis puslapis

Language Models Machine Learning Optimization Algorithms Theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
LoRA: Low-rank adaptation of large language models EJ Hu, Y Shen, P Wallis, Z Allen-Zhu, Y Li, S Wang, L Wang, W Chen ICLR 2022: International Conference on Learning Representations, 2022	12072	2022
A convergence theory for deep learning via over-parameterization Z Allen-Zhu, Y Li, Z Song ICML 2019: International Conference on Machine Learning, 2019	1743	2019
Is Q-learning Provably Efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan NIPS 2018: Neural Information Processing Systems, 2018	1026	2018
Learning and generalization in overparameterized neural networks, going beyond two layers Z Allen-Zhu, Y Li, Y Liang NeurIPS 2019: Neural Information Processing Systems, 2019	930	2019
Katyusha: the first direct acceleration of stochastic gradient methods Z Allen-Zhu STOC 2017: Symposium on Theory of Computing, 19-23, 2017	726	2017
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning Z Allen-Zhu, Y Li ICLR 2023: International Conference on Learning Representations, 2023	485	2023
Variance reduction for faster non-convex optimization Z Allen-Zhu, E Hazan ICML 2016: International Conference on Machine Learning, 699-707, 2016	456	2016
Linear coupling: An ultimate unification of gradient and mirror descent Z Allen-Zhu, L Orecchia ITCS 2017: Innovations in Theoretical Computer Science, 2017	396	2017
Finding approximate local minima faster than gradient descent N Agarwal, Z Allen-Zhu, B Bullins, E Hazan, T Ma STOC 2017: Symposium on Theory of Computing, 1195-1199, 2017	370*	2017
Byzantine Stochastic Gradient Descent D Alistarh, Z Allen-Zhu, J Li NIPS 2018: Neural Information Processing Systems, 2018	355	2018
A simple, combinatorial algorithm for solving SDD systems in nearly-linear time JA Kelner, L Orecchia, A Sidford, ZA Zhu STOC 2013: Symposium on Theory of Computing, 911-920, 2013	304	2013
Natasha 2: Faster Non-Convex Optimization Than SGD Z Allen-Zhu NIPS 2018: Neural Information Processing Systems, 2018	270	2018
Improved SVRG for non-strongly-convex or sum-of-non-convex objectives Z Allen-Zhu, Y Yuan ICML 2016: International Conference on Machine Learning, 1080-1089, 2016	244	2016
What Can ResNet Learn Efficiently, Going Beyond Kernels? Z Allen-Zhu, Y Li NeurIPS 2019: Neural Information Processing Systems, 2019	238	2019
On the convergence rate of training recurrent neural networks Z Allen-Zhu, Y Li, Z Song NeurIPS 2019: Neural Information Processing Systems, 2019	217	2019
Asymptotically optimal strategy-proof mechanisms for two-facility games P Lu, X Sun, Y Wang, ZA Zhu ACM-EC 2010: Conference on Economics and Computation, 315-324, 2010	215	2010
Even faster accelerated coordinate descent using non-uniform sampling Z Allen-Zhu, Z Qu, P Richtárik, Y Yuan ICML 2016: International Conference on Machine Learning, 1110-1119, 2016	212	2016
Feature purification: How adversarial training performs robust deep learning Z Allen-Zhu, Y Li FOCS 2021: Symposium on Foundations of Computer Science, 977-988, 2022	193	2022
Neon2: Finding Local Minima via First-Order Oracles Z Allen-Zhu, Y Li NIPS 2018: Neural Information Processing Systems, 2018	164	2018
LazySVD: Even faster SVD decomposition yet without agonizing pain Z Allen-Zhu, Y Li NIPS 2016: Neural Information Processing Systems, 974-982, 2016	149	2016

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