Jerry Li

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Dan AlistarhIST AustriaDirección de correo verificada de ist.ac.at
Sitan ChenAssistant Professor of Computer Science, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonDirección de correo verificada de wisc.edu
Milan VojnovicProfessor, Department of Statistics, LSEDirección de correo verificada de lse.ac.uk
Ryota TomiokaMicrosoft Research AI for ScienceDirección de correo verificada de microsoft.com
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector InstituteDirección de correo verificada de uwaterloo.ca
Daniel KaneUniversity of California, San DiegoDirección de correo verificada de ucsd.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITDirección de correo verificada de mit.edu
Ludwig SchmidtStanford University and AnthropicDirección de correo verificada de cs.washington.edu
Alistair StewartWeb3 foundationDirección de correo verificada de web3.foundation
Kevin TianAssistant Professor, University of Texas at AustinDirección de correo verificada de cs.utexas.edu
Samuel B. HopkinsMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Jordan CotlerHarvard UniversityDirección de correo verificada de fas.harvard.edu
Hsin-Yuan Huang (Robert)California Institute of Technology, Google Quantum AIDirección de correo verificada de caltech.edu
Aleksander MądryMITDirección de correo verificada de mit.edu
Allen LiuMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Ilya RazenshteynCipherMode LabsDirección de correo verificada de mit.edu
Demjan GrubicGoogleDirección de correo verificada de google.com
Hadi SalmanOpenAI, MITDirección de correo verificada de mit.edu
Greg YangxAIDirección de correo verificada de x.ai

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Jerry Li

University of Washington

Dirección de correo verificada de cs.washington.edu - Página principal

Learning Theory Machine Learning Distributed Computing Quantum Information Theory


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in Neural Information Processing Systems, 1707-1718, 2017	2018	2017
Spectral signatures in backdoor attacks B Tran, J Li, A Madry Advances in neural information processing systems 31, 2018	883	2018
Provably robust deep learning via adversarially trained smoothed classifiers H Salman, J Li, I Razenshteyn, P Zhang, H Zhang, S Bubeck, G Yang Advances in neural information processing systems 32, 2019	604	2019
Robust estimators in high-dimensions without the computational intractability I Diakonikolas, G Kamath, D Kane, J Li, A Moitra, A Stewart SIAM Journal on Computing 48 (2), 742-864, 2019	545	2019
Quantum advantage in learning from experiments HY Huang, M Broughton, J Cotler, S Chen, J Li, M Mohseni, H Neven, ... Science 376 (6598), 1182-1186, 2022	513	2022
Aligning ai with shared human values D Hendrycks, C Burns, S Basart, A Critch, J Li, D Song, J Steinhardt arXiv preprint arXiv:2008.02275, 2020	436	2020
Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in neural information processing systems 31, 2018	346	2018
Sever: A robust meta-algorithm for stochastic optimization I Diakonikolas, G Kamath, D Kane, J Li, J Steinhardt, A Stewart International Conference on Machine Learning, 1596-1606, 2019	337	2019
Being robust (in high dimensions) can be practical I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart International Conference on Machine Learning, 999-1008, 2017	274	2017
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions S Chen, S Chewi, J Li, Y Li, A Salim, AR Zhang arXiv preprint arXiv:2209.11215, 2022	258	2022
ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang International Conference on Machine Learning, 4035-4043, 2017	253*	2017
Randomized smoothing of all shapes and sizes G Yang, T Duan, JE Hu, H Salman, I Razenshteyn, J Li International Conference on Machine Learning, 10693-10705, 2020	225	2020
Automatic prompt optimization with" gradient descent" and beam search R Pryzant, D Iter, J Li, YT Lee, C Zhu, M Zeng arXiv preprint arXiv:2305.03495, 2023	220	2023
Mixture models, robustness, and sum of squares proofs SB Hopkins, J Li Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing …, 2018	202	2018
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	166	2019
The spraylist: A scalable relaxed priority queue D Alistarh, J Kopinsky, J Li, N Shavit Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015	151	2015
Robustly learning a gaussian: Getting optimal error, efficiently I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018	147	2018
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	141	2017
Exponential separations between learning with and without quantum memory S Chen, J Cotler, HY Huang, J Li 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022	129	2022
Quantum entropy scoring for fast robust mean estimation and improved outlier detection Y Dong, S Hopkins, J Li Advances in Neural Information Processing Systems 32, 2019	112	2019

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