Sashank J. Reddi

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Sanjiv KumarGoogle Fellow, VP, Google ResearchVerified email at google.com
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Satyen KaleStaff Research Scientist, Google ResearchVerified email at google.com
Suvrit SraMITVerified email at mit.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Ahmed HefnySoftware Engineer, WaymoVerified email at cs.cmu.edu
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 SinghAssociate Professor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Kumar Avinava DubeyResearch Scientist, Google 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
Nisarg ShahAssistant Professor, University of TorontoVerified email at cs.toronto.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
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

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	1730	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	499	2016
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	419	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	372	2019
Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020	293	2020
Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems 31, 2018	213	2018
Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 2016	188	2016
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization S J. Reddi, S Sra, B Poczos, A Smola arXiv:1605.06900, 2016	182*	2016
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	175	2015
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	130	2015
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. SP Karimireddy, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh	129	2019
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	126	2016
Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016	120	2016
A maximum likelihood approach for selecting sets of alternatives AD Procaccia, SJ Reddi, N Shah arXiv preprint arXiv:1210.4882, 2012	81	2012
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	77	2016
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	67	2019
Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International Conference on Learning Representations, 2019	65	2019
A generic approach for escaping saddle points S Reddi, M Zaheer, S Sra, B Poczos, F Bach, R Salakhutdinov, A Smola International conference on artificial intelligence and statistics, 1233-1242, 2018	62	2018
Fast incremental method for smooth nonconvex optimization SJ Reddi, S Sra, B Póczos, A Smola 2016 IEEE 55th Conference on Decision and Control (CDC), 1971-1977, 2016	60*	2016
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	53	2020

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