Sashank J. Reddi

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Sanjiv KumarGoogle Fellow, VP, Google ResearchEmail confirmado em google.com
Satyen KaleResearch Scientist, AppleEmail confirmado em satyenkale.com
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityEmail confirmado em cs.cmu.edu
Suvrit SraTUM and MITEmail confirmado em mit.edu
Alex SmolaBoson AIEmail confirmado em smola.org
Ahmed HefnyApplied Research Scientist, CruiseEmail confirmado em getcruise.com
Jakub KonečnýResearch Scientist, GoogleEmail confirmado em google.com
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityEmail confirmado em stat.cmu.edu
Aaditya RamdasAssociate Professor, Statistics and Machine Learning, Carnegie Mellon UniversityEmail confirmado em stat.cmu.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityEmail confirmado em cmu.edu
Kumar Avinava DubeyGoogle ResearchEmail confirmado em google.com
Hongyi ZhangByteDance Inc.Email confirmado em bytedance.com
Ariel ProcacciaGordon McKay Professor of Computer Science, Harvard UniversityEmail confirmado em seas.harvard.edu
Peter RichtarikProfessor, KAUSTEmail confirmado em kaust.edu.sa
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UEmail confirmado em cs.cmu.edu
Sinead WilliamsonAssistant professor, University of Texas at AustinEmail confirmado em mccombs.utexas.edu
Nisarg ShahAssociate Professor, University of TorontoEmail confirmado em cs.toronto.edu
Francis BachInria - Ecole Normale SupérieureEmail confirmado em inria.fr
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUEmail confirmado em cs.cmu.edu
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityEmail confirmado em cs.stanford.edu

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Sashank J. Reddi

Outros nomesSashank Reddi, Sashank Jakkam Reddi

Research Scientist, Google Research

Email confirmado em cs.cmu.edu - Página inicial

Machine Learning Optimization Statistics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
On the convergence of adam and beyond SJ Reddi, S Kale, S Kumar arXiv preprint arXiv:1904.09237, 2019	3207	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	2993	2020
Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020	1519	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	1061	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	683	2016
Adaptive methods for nonconvex optimization M Zaheer, S Reddi, D Sachan, S Kale, S Kumar Advances in neural information processing systems 31, 2018	513	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	381	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	359	2019
Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds H Zhang, S J Reddi, S Sra Advances in Neural Information Processing Systems 29, 2016	286	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	281	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	253	2020
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	216	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
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	205	2015
Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016	180	2016
Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International Conference on Learning Representations, 2020	174	2020
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	172	2016
Adaclip: Adaptive clipping for private sgd V Pichapati, AT Suresh, FX Yu, SJ Reddi, S Kumar arXiv preprint arXiv:1908.07643, 2019	138	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	108	2016

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