Virginia Smith
Cited by
Cited by
Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE Signal Processing Magazine 37 (3), 50-60, 2020
Federated multi-task learning
V Smith, CK Chiang, M Sanjabi, A Talwalkar
Advances in Neural Information Processing Systems, 2017
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
Conference on Machine Learning and Systems, 2020
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
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
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
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
International Conference on Learning Representations, 2020
Adding vs. averaging in distributed primal-dual optimization
C Ma*, V Smith*, M Jaggi, MI Jordan, P Richtárik, ...
International Conference on Machine Learning, 2015
CoCoA: A general framework for communication-efficient distributed optimization
V Smith, S Forte, M Chenxin, M Takáč, MI Jordan, M Jaggi
Journal of Machine Learning Research 18, 230, 2018
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
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
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
One-shot federated learning
N Guha, A Talwalkar, V Smith
arXiv preprint arXiv:1902.11175, 2019
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
Privacy for free: Communication-efficient learning with differential privacy using sketches
T Li, Z Liu, V Sekar, V Smith
arXiv preprint arXiv:1911.00972, 2019
Federated kernelized multi-task learning
S Caldas, V Smith, A Talwalkar
Proc. SysML Conf., 1-3, 2018
L1-regularized distributed optimization: A communication-efficient primal-dual framework
V Smith, S Forte, MI Jordan, M Jaggi
arXiv preprint arXiv:1512.04011, 2015
Modeling building thermal response to HVAC zoning
V Smith, T Sookoor, K Whitehouse
ACM SIGBED Review 9 (3), 39-45, 2012
Mlbase: A distributed machine learning wrapper
A Talwalkar, T Kraska, R Griffith, J Duchi, J Gonzalez, D Britz, X Pan, ...
NIPS Big Learning Workshop, 35, 2012
Efficient augmentation via data subsampling
M Kuchnik, V Smith
arXiv preprint arXiv:1810.05222, 2018
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