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Virginia Smith
Virginia Smith
Carnegie Mellon University
Geverifieerd e-mailadres voor cmu.edu
Titel
Geciteerd door
Geciteerd door
Jaar
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
Conference on Machine Learning and Systems, 2020
56442020
Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE signal processing magazine 37 (3), 50-60, 2020
54002020
Federated multi-task learning
V Smith, CK Chiang, M Sanjabi, A Talwalkar
Advances in Neural Information Processing Systems, 2017
21672017
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
14932018
Ditto: Fair and robust federated learning through personalization
T Li, S Hu, A Beirami, V Smith
International conference on machine learning, 6357-6368, 2021
9302021
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
International Conference on Learning Representations, 2020
9292020
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
4192014
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
3812021
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
3212018
One-shot federated learning
N Guha, A Talwalkar, V Smith
arXiv preprint arXiv:1902.11175, 2019
2502019
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
2462013
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
2332017
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
2282019
Adding vs. averaging in distributed primal-dual optimization
C Ma*, V Smith*, M Jaggi, MI Jordan, P Richtárik, ...
International Conference on Machine Learning, 2015
2102015
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
1842019
Heterogeneity for the win: One-shot federated clustering
DK Dennis, T Li, V Smith
International Conference on Machine Learning, 2611-2620, 2021
1642021
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
1542021
Tilted empirical risk minimization
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2007.01162, 2020
1492020
Diverse client selection for federated learning via submodular maximization
R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes
International Conference on Learning Representations, 2022
1152022
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
1142021
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Artikelen 1–20