Sashank J. Reddi

Cited by

	All	Since 2016
Citations	3172	3073
h-index	23	22
i10-index	33	33

1300

650

325

975

2014201520162017201820192020202126 41 120 222 487 897 1288 27

Co-authors

Sanjiv KumarDistinguished Scientist, GoogleVerified email at google.com
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Suvrit SraMITVerified email at mit.edu
Satyen KaleStaff Research Scientist, Google ResearchVerified email at google.com
Alex SmolaAmazon Web ServicesVerified email at smola.org
Ahmed HefnySoftware Engineer, WaymoVerified email at cs.cmu.edu
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
Jakub KonečnýResearch Scientist, GoogleVerified email at google.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
Avinava DubeyCarnegie Mellon UniversityVerified email at cs.cmu.edu
Hongyi ZhangByteDance Inc.Verified email at bytedance.com
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
Eric XingProfessor of Machine Learning, Language Technology, Computer Science, Cargenie Mellon UniversityVerified 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
Sunita SarawagiIndian Institute of Technology Bombay, Mumbai, India.Verified email at iitb.ac.in
Sundar vishwanathanProfessor of Computer Science and Engineering, IIT BombayVerified email at cse.iitb.ac.in

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	1035	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	410	2016
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization S J. Reddi, S Sra, B Poczos, A Smola arXiv:1605.06900, 2016	160*	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	156	2015
Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 4592-4600, 2016	142	2016
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	126	2019
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	106	2015
Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems, 9793-9803, 2018	89	2018
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	87	2016
Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016	85	2016
A maximum likelihood approach for selecting sets of alternatives AD Procaccia, SJ Reddi, N Shah arXiv preprint arXiv:1210.4882, 2012	78	2012
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	74*	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, 2019	65	2019
Variance reduction in stochastic gradient Langevin dynamics A Dubey, SJ Reddi, B Póczos, AJ Smola, EP Xing, SA Williamson Advances in neural information processing systems 29, 1154, 2016	58	2016
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	46	2018
Adaptive Federated Optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020	41	2020
On the high dimensional power of a linear-time two sample test under mean-shift alternatives S Reddi, A Ramdas, B Póczos, A Singh, L Wasserman Artificial Intelligence and Statistics, 772-780, 2015	40*	2015
Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, and Alexander J Smola. A generic approach for escaping saddle points SJ Reddi, M Zaheer arXiv preprint arXiv:1709.01434, 2017	30	2017
Doubly robust covariate shift correction S Reddi, B Poczos, A Smola Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	30	2015
Why ADAM beats SGD for attention models J Zhang, SP Karimireddy, A Veit, S Kim, SJ Reddi, S Kumar, S Sra arXiv preprint arXiv:1912.03194, 2019	28	2019

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