H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
Verified email at google.com - Homepage
TitleCited byYear
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
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
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
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, AT Kalai, HB McMahan
Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete …, 2005
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
Robust submodular observation selection
A Krause, HB McMahan, C Guestrin, A Gupta
Journal of Machine Learning Research 9 (Dec), 2761-2801, 2008
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
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
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
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
Online geometric optimization in the bandit setting against an adaptive adversary
HB McMahan, A Blum
International Conference on Computational Learning Theory, 109-123, 2004
Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
HB McMahan
Proceedings of the 14th International Conference on Artificial Intelligence …, 2011
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
Estimation, optimization, and parallelism when data is sparse
J Duchi, MI Jordan, B McMahan
Advances in Neural Information Processing Systems, 2832-2840, 2013
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
Delay-tolerant algorithms for asynchronous distributed online learning
B McMahan, M Streeter
Advances in Neural Information Processing Systems, 2915-2923, 2014
Selecting observations against adversarial objectives
A Krause, B McMahan, C Guestrin, A Gupta
Advances in Neural Information Processing Systems, 777-784, 2008
Sleeping experts and bandits with stochastic action availability and adversarial rewards
V Kanade, HB McMahan, B Bryan
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