Virginia Smith

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	All	Since 2018
Citations	14086	13617
h-index	27	27
i10-index	46	44

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Tian LiCarnegie Mellon UniversityVerified email at cmu.edu
Maziar SanjabiMeta AIVerified email at fb.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Ahmad BeiramiGoogle ResearchVerified email at google.com
Martin JaggiEPFLVerified email at epfl.ch
Martin TakáčMohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at mbzuai.ac.ae
Chenxin MaPhD, Lehigh UniversityVerified email at lehigh.edu
Jakub KonečnýResearch Scientist, GoogleVerified email at google.com
H. Brendan McMahanResearch Scientist, Google SeattleVerified email at google.com
Chao-Kai ChiangProject Assistant Professor, Sugiyama-Yokoya-Ishida Lab, University of TokyoVerified email at edu.k.u-tokyo.ac.jp
Vyas SekarTan Family Chair in Electrical and Computer Engineering, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Peter WuSchool of Computer Science, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Peter RichtarikProfessor, KAUSTVerified email at kaust.edu.sa
Sanjay KrishnanUniversity of ChicagoVerified email at cs.uchicago.edu
Michael FranklinLiew Family Chair of Computer Science, University of ChicagoVerified email at uchicago.edu
Xinghao PanVerified email at eecs.berkeley.edu
Tim KraskaMITVerified email at mit.edu
David CULLERUniversity of California, BerkeleyVerified email at berkeley.edu
Alex RatnerAssistant Professor, University of WashingtonVerified email at cs.washington.edu

Virginia Smith

Carnegie Mellon University

Verified email at cmu.edu

Machine Learning Optimization Distributed Systems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020	3697	2020
Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith Conference on Machine Learning and Systems, 2020	3346	2020
Federated multi-task learning V Smith, CK Chiang, M Sanjabi, A Talwalkar Advances in Neural Information Processing Systems, 2017	1663	2017
Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018	1066	2018
Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith International Conference on Learning Representations, 2020	665	2020
Ditto: Fair and robust federated learning through personalization T Li, S Hu, A Beirami, V Smith International Conference on Machine Learning, 6357-6368, 2021	479	2021
Communication-efficient distributed dual coordinate ascent M Jaggi, V Smith, M Takác, J Terhorst, S Krishnan, T Hofmann, ... Advances in Neural Information Processing Systems, 2014	402	2014
CoCoA: A general framework for communication-efficient distributed optimization V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi Journal of Machine Learning Research 18 (230), 1-49, 2018	280	2018
MLI: An API for distributed machine learning ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ... IEEE International Conference on Data Mining, 2013	240	2013
Distributed optimization with arbitrary local solvers C Ma, J Konečný, M Jaggi, V Smith, MI Jordan, P Richtárik, M Takáč optimization Methods and Software 32 (4), 813-848, 2017	206	2017
Adding vs. averaging in distributed primal-dual optimization C Ma, V Smith, M Jaggi, MI Jordan, P Richtárik, ... International Conference on Machine Learning, 2015	200	2015
One-shot federated learning N Guha, A Talwalkar, V Smith arXiv preprint arXiv:1902.11175, 2019	181	2019
A kernel theory of modern data augmentation T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré International conference on machine learning, 1528-1537, 2019	176	2019
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	155	2021
Feddane: A federated newton-type method T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smithy 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1227-1231, 2019	125	2019
Tilted empirical risk minimization T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2007.01162, 2020	106	2020
Identifying models of HVAC systems using semiparametric regression A Aswani, N Master, J Taneja, V Smith, A Krioukov, D Culler, C Tomlin 2012 American Control Conference (ACC), 3675-3680, 2012	106	2012
Label leakage and protection in two-party split learning O Li, J Sun, X Yang, W Gao, H Zhang, J Xie, V Smith, C Wang arXiv preprint arXiv:2102.08504, 2021	93	2021
Heterogeneity for the win: One-shot federated clustering DK Dennis, T Li, V Smith International Conference on Machine Learning, 2611-2620, 2021	89	2021
Learning context-aware policies from multiple smart homes via federated multi-task learning T Yu, T Li, Y Sun, S Nanda, V Smith, V Sekar, S Seshan 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design …, 2020	70	2020

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