Zachary Charles

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Zachary GarrettGoogleEmail yang diverifikasi di google.com
Dimitris PapailiopoulosResearcher at Microsoft Research & Assoc. Professor at University of WisconsinEmail yang diverifikasi di papail.io
Keith RushGoogleEmail yang diverifikasi di google.com
Jakub KonečnýResearch Scientist, GoogleEmail yang diverifikasi di google.com
H. Brendan McMahanResearch Scientist, Google SeattleEmail yang diverifikasi di google.com
Hongyi WangGenBio AI; Incoming Assistant Professor, Rutgers UniversityEmail yang diverifikasi di genbio.ai
Sashank J. ReddiResearch Scientist, Google ResearchEmail yang diverifikasi di cs.cmu.edu
Manzil ZaheerGoogle ResearchEmail yang diverifikasi di cmu.edu
Sanjiv KumarGoogle Fellow, VP, Google ResearchEmail yang diverifikasi di google.com
Stephen WrightDepartment of Computer Sciences and Wisconsin Institute for Discovery, University of WisconsinEmail yang diverifikasi di cs.wisc.edu
Nicole MitchellGoogle ResearchEmail yang diverifikasi di google.com
Shashank RajputResearch Scientist, MosaicML (Databricks)Email yang diverifikasi di databricks.com
Lingjiao ChenStanford UniversityEmail yang diverifikasi di stanford.edu
Shengchao LiuUC BerkeleyEmail yang diverifikasi di berkeley.edu
Virginia SmithCarnegie Mellon UniversityEmail yang diverifikasi di cmu.edu
Zheng XuGoogle ResearchEmail yang diverifikasi di google.com
miriam farberResearch scientistEmail yang diverifikasi di meta.com
Ryan McKennaResearch Scientist, GoogleEmail yang diverifikasi di google.com
Zhouyuan HuoGoogleEmail yang diverifikasi di google.com
Jordan EllenbergProfessor of Mathematics, University of Wisconsin and Wisconsin Institute for DiscoveryEmail yang diverifikasi di math.wisc.edu

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Zachary Charles

Research Scientist, Google

Email yang diverifikasi di google.com - Beranda

Machine Learning Optimization Federated Learning Applied Mathematics


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and trends® in machine learning 14 (1–2), 1-210, 2021	7216	2021
Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020	1709	2020
Atomo: Communication-efficient learning via atomic sparsification H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright Advances in neural information processing systems 31, 2018	421	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	416	2021
Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018	302	2018
Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021	213	2021
Stability and generalization of learning algorithms that converge to global optima Z Charles, D Papailiopoulos International conference on machine learning, 745-754, 2018	190	2018
DETOX: A redundancy-based framework for faster and more robust gradient aggregation S Rajput, H Wang, Z Charles, D Papailiopoulos Advances in neural information processing systems 32, 2019	144	2019
On large-cohort training for federated learning Z Charles, Z Garrett, Z Huo, S Shmulyian, V Smith Advances in neural information processing systems 34, 20461-20475, 2021	124	2021
Approximate gradient coding via sparse random graphs Z Charles, D Papailiopoulos, J Ellenberg arXiv preprint arXiv:1711.06771, 2017	93	2017
Convergence and accuracy trade-offs in federated learning and meta-learning Z Charles, J Konečný International Conference on Artificial Intelligence and Statistics, 2575-2583, 2021	86	2021
Advances and open problems in federated learning. arXiv 2019 P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 1912	67	1912
Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019	65	2019
On the outsized importance of learning rates in local update methods Z Charles, J Konečný arXiv preprint arXiv:2007.00878, 2020	63	2020
Local adaptivity in federated learning: Convergence and consistency J Wang, Z Xu, Z Garrett, Z Charles, L Liu, G Joshi arXiv preprint arXiv:2106.02305, 2021	53	2021
Gradient coding using the stochastic block model Z Charles, D Papailiopoulos 2018 IEEE International Symposium on Information Theory (ISIT), 1998-2002, 2018	52*	2018
Motley: Benchmarking heterogeneity and personalization in federated learning S Wu, T Li, Z Charles, Y Xiao, Z Liu, Z Xu, V Smith arXiv preprint arXiv:2206.09262, 2022	51	2022
Does data augmentation lead to positive margin? S Rajput, Z Feng, Z Charles, PL Loh, D Papailiopoulos International Conference on Machine Learning, 5321-5330, 2019	47	2019
Optimizing the communication-accuracy trade-off in federated learning with rate-distortion theory N Mitchell, J Ballé, Z Charles, J Konečný arXiv preprint arXiv:2201.02664, 2022	27	2022
Towards federated foundation models: Scalable dataset pipelines for group-structured learning Z Charles, N Mitchell, K Pillutla, M Reneer, Z Garrett Advances in Neural Information Processing Systems 36, 32299-32327, 2023	26	2023

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