Sashank J. Reddi

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Sanjiv KumarGoogle Fellow, VP, Google ResearchVerified email at google.com
Satyen KaleResearch Scientist, Google ResearchVerified email at google.com
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Suvrit SraTUM and MITVerified email at mit.edu
Alex SmolaBoson AIVerified email at smola.org
Ahmed HefnyApplied Research Scientist, CruiseVerified email at getcruise.com
Jakub KonečnýResearch Scientist, GoogleVerified email at google.com
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Aaditya RamdasStatistics and Machine Learning, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Kumar Avinava DubeyGoogle ResearchVerified email at google.com
Hongyi ZhangByteDance Inc.Verified email at bytedance.com
Ariel ProcacciaGordon McKay Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Sinead WilliamsonAssistant professor, University of Texas at AustinVerified email at mccombs.utexas.edu
Nisarg ShahAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu

Sashank J. Reddi

Other namesSashank Reddi, Sashank Jakkam Reddi

Research Scientist, Google Research

Verified email at cs.cmu.edu - Homepage

Machine Learning Optimization Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
On the convergence of adam and beyond SJ Reddi, S Kale, S Kumar arXiv preprint arXiv:1904.09237, 2019	3092	2019
Scaffold: Stochastic controlled averaging for federated learning SP Karimireddy, S Kale, M Mohri, S Reddi, S Stich, AT Suresh International conference on machine learning, 5132-5143, 2020	2719	2020
Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020	1411	2020
Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962, 2019	1012	2019
Stochastic variance reduction for nonconvex optimization SJ Reddi, A Hefny, S Sra, B Poczos, A Smola International conference on machine learning, 314-323, 2016	675	2016
Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems 31, 2018	486	2018
A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021	363	2021
Are transformers universal approximators of sequence-to-sequence functions? C Yun, S Bhojanapalli, AS Rawat, SJ Reddi, S Kumar arXiv preprint arXiv:1912.10077, 2019	330	2019
Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 2016	279	2016
Scaffold: Stochastic controlled averaging for on-device federated learning SP Karimireddy, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh arXiv preprint arXiv:1910.06378 2 (6), 2019	272	2019
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization S J. Reddi, S Sra, B Poczos, A Smola arXiv:1605.06900, 2016	260*	2016
Why are adaptive methods good for attention models? J Zhang, SP Karimireddy, A Veit, S Kim, S Reddi, S Kumar, S Sra Advances in Neural Information Processing Systems 33, 15383-15393, 2020	225	2020
On variance reduction in stochastic gradient descent and its asynchronous variants SJ Reddi, A Hefny, S Sra, B Poczos, AJ Smola Advances in neural information processing systems, 2647-2655, 2015	208	2015
Mime: Mimicking centralized stochastic algorithms in federated learning SP Karimireddy, M Jaggi, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh arXiv preprint arXiv:2008.03606, 2020	205	2020
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions A Ramdas, SJ Reddi, B Póczos, A Singh, L Wasserman Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	201	2015
Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016	177	2016
Stochastic frank-wolfe methods for nonconvex optimization SJ Reddi, S Sra, B Póczos, A Smola 2016 54th annual Allerton conference on communication, control, and …, 2016	170	2016
Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International Conference on Learning Representations, 2020	168	2020
Adaclip: Adaptive clipping for private sgd V Pichapati, AT Suresh, FX Yu, SJ Reddi, S Kumar arXiv preprint arXiv:1908.07643, 2019	129	2019
Variance reduction in stochastic gradient Langevin dynamics KA Dubey, S J Reddi, SA Williamson, B Poczos, AJ Smola, EP Xing Advances in neural information processing systems 29, 2016	104	2016

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