McMahan, Brendan

17 publications

NeurIPS 2021 Differentially Private Learning with Adaptive Clipping Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy

ICML 2021 Practical and Private (Deep) Learning Without Sampling or Shuffling Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

AISTATS 2020 Federated Heavy Hitters Discovery with Differential Privacy Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li

ICML 2020 Is Local SGD Better than Minibatch SGD? Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro

NeurIPS 2020 Privacy Amplification via Random Check-Ins Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, Abhradeep Guha Thakurta

ICML 2019 Semi-Cyclic Stochastic Gradient Descent Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar

NeurIPS 2018 Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, Nati Srebro

NeurIPS 2018 cpSGD: Communication-Efficient and Differentially-Private Distributed SGD Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan McMahan

AISTATS 2017 Communication-Efficient Learning of Deep Networks from Decentralized Data Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

NeurIPS 2014 Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter

NeurIPS 2013 Estimation, Optimization, and Parallelism When Data Is Sparse John Duchi, Michael I Jordan, Brendan McMahan

ICML 2013 Large-Scale Learning with Less RAM via Randomization Daniel Golovin, D. Sculley, Brendan McMahan, Michael Young

NeurIPS 2013 Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy

NeurIPS 2012 No-Regret Algorithms for Unconstrained Online Convex Optimization Brendan Mcmahan, Matthew Streeter

AISTATS 2011 Discussion of “Contextual Bandit Algorithms with Supervised Learning Guarantees” Brendan McMahan

AISTATS 2011 Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization Brendan McMahan

NeurIPS 2007 Selecting Observations Against Adversarial Objectives Andreas Krause, Brendan Mcmahan, Carlos Guestrin, Anupam Gupta