Jerry Li

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Dan AlistarhProfessor at IST AustriaPatvirtintas el. paštas ist.ac.at
Sitan ChenAssistant Professor of Computer Science, Harvard UniversityPatvirtintas el. paštas seas.harvard.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonPatvirtintas el. paštas wisc.edu
Milan VojnovicProfessor, Department of Statistics, LSEPatvirtintas el. paštas lse.ac.uk
Ryota TomiokaMicrosoft Research AI for SciencePatvirtintas el. paštas microsoft.com
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector InstitutePatvirtintas el. paštas uwaterloo.ca
Daniel KaneUniversity of California, San DiegoPatvirtintas el. paštas ucsd.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITPatvirtintas el. paštas mit.edu
Ludwig SchmidtStanford University and AnthropicPatvirtintas el. paštas stanford.edu
Alistair StewartWeb3 foundationPatvirtintas el. paštas web3.foundation
Kevin TianAssistant Professor, University of Texas at AustinPatvirtintas el. paštas cs.utexas.edu
Samuel B. HopkinsMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Jordan CotlerHarvard UniversityPatvirtintas el. paštas fas.harvard.edu
Hsin-Yuan Huang (Robert)California Institute of Technology, Google Quantum AIPatvirtintas el. paštas caltech.edu
Allen LiuMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Aleksander MądryMITPatvirtintas el. paštas mit.edu
Ilya RazenshteynCipherMode LabsPatvirtintas el. paštas mit.edu
Yuanzhi LiAssistant Professor at CMUPatvirtintas el. paštas andrew.cmu.edu
Demjan GrubicGooglePatvirtintas el. paštas google.com
Jacob SteinhardtStanford UniversityPatvirtintas el. paštas cs.stanford.edu

Stebėti

Jerry Li

University of Washington

Patvirtintas el. paštas cs.washington.edu - Pagrindinis puslapis

Learning Theory Machine Learning Distributed Computing Quantum Information Theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	2118	2017
Spectral signatures in backdoor attacks B Tran, J Li, A Madry Advances in neural information processing systems 31, 2018	934	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	617	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	571	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	535	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	500	2020
Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in neural information processing systems 31, 2018	355	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	350	2019
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	314	2022
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	295	2023
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	288	2017
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	255*	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	230	2020
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	211	2018
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	178	2019
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	154	2018
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
Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017	147	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	143	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	118	2019

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