H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
google.com의 이메일 확인됨 - 홈페이지
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Communication-efficient learning of deep networks from decentralized data
HB McMahan, E Moore, D Ramage, S Hampson, B Agüera y Arcas
Proceedings of the 20 th International Conference on Artificial Intelligence …, 2017
2282*2017
Deep learning with differential privacy
M Abadi, A Chu, I Goodfellow, HB McMahan, I Mironov, K Talwar, L Zhang
Proceedings of the 2016 ACM SIGSAC conference on computer and communications …, 2016
17622016
Federated learning: Strategies for improving communication efficiency
J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon
arXiv preprint arXiv:1610.05492, 2016
12492016
Ad click prediction: a view from the trenches
HB McMahan, G Holt, D Sculley, M Young, D Ebner, J Grady, L Nie, ...
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
7122013
Practical secure aggregation for privacy-preserving machine learning
K Bonawitz, V Ivanov, B Kreuter, A Marcedone, HB McMahan, S Patel, ...
proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017
7082017
Towards federated learning at scale: System design
K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ...
arXiv preprint arXiv:1902.01046, 2019
5902019
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, HB McMahan
arXiv preprint cs/0408007, 2004
5752004
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 2019
5492019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
5122016
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
357*2017
Robust Submodular Observation Selection.
A Krause, HB McMahan, C Guestrin, A Gupta
Journal of Machine Learning Research 9 (12), 2008
2812008
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
2592017
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
2312015
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
2272010
Planning in the presence of cost functions controlled by an adversary
HB McMahan, GJ Gordon, A Blum
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
2272003
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
HB McMahan, M Likhachev, GJ Gordon
Proceedings of the 22nd international conference on Machine learning, 569-576, 2005
1762005
Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
HB McMahan
Proceedings of the 14th International Conference on Artificial Intelligence …, 2011
1752011
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
1652018
cpSGD: Communication-efficient and differentially-private distributed SGD
N Agarwal, AT Suresh, F Yu, S Kumar, HB Mcmahan
arXiv preprint arXiv:1805.10559, 2018
1582018
Online geometric optimization in the bandit setting against an adaptive adversary
HB McMahan, A Blum
International Conference on Computational Learning Theory, 109-123, 2004
1502004
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