Brendan Mcmahan
18 papers · 2007–2023 · 4 conferences · across top CS/AI conferences
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(18)
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Conferences
NIPS (9)
AISTATS (4)
ICML (4)
ACL (1)
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Research topics
Keywords
federated learning
(8)
differential privacy
(6)
stochastic gradient descent
(4)
convex optimization
(3)
distributed learning
(3)
regret bound
(3)
distributed optimization
(2)
online learning
(2)
unconstrained optimization
(2)
online convex optimization
(2)
communication efficiency
(2)
sample complexity
(1)
randomized rounding
(1)
supervised learning
(1)
game theory
(1)
active learning
(1)
gaussian process
(1)
regret minimization
(1)
l1 regularization
(1)
privacy-preserving learning
(1)
Papers
Federated Learning of Gboard Language Models with Differential Privacy
ACL 2023
Practical and Private (Deep) Learning Without Sampling or Shuffling
ICML 2021
Differentially Private Learning with Adaptive Clipping
NIPS 2021
Federated Heavy Hitters Discovery with Differential Privacy
AISTATS 2020
Privacy Amplification via Random Check-Ins
NIPS 2020
Is Local SGD Better than Minibatch SGD?
ICML 2020
Semi-Cyclic Stochastic Gradient Descent
ICML 2019
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
NIPS 2018
cpSGD: Communication-efficient and differentially-private distributed SGD
NIPS 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data
AISTATS 2017
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
NIPS 2014
Minimax Optimal Algorithms for Unconstrained Linear Optimization
NIPS 2013
Large-Scale Learning with Less RAM via Randomization
ICML 2013
Estimation, Optimization, and Parallelism when Data is Sparse
NIPS 2013
No-Regret Algorithms for Unconstrained Online Convex Optimization
NIPS 2012
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization
AISTATS 2011
Discussion of βContextual Bandit Algorithms with Supervised Learning Guaranteesβ
AISTATS 2011
Selecting Observations against Adversarial Objectives
NIPS 2007