Brendan Mcmahan
18 papers · 2007–2023 · 4 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (33) 🏃 Academic Marathon · Full 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🌱
Topic Pioneer
🌟
Keyword Trendsetter Combo
(10)
🏆
Keyword Champion
(2)
🧭
Keyword Pioneer
❓
The Questioner
🔥
Unstoppable · Bronze
💎
Century Club · Bronze
🗃️
Keyword Collector
(86)
🚀
Conference Pioneer
📈
Trend Setter
Venues
Top co-authors
Research topics
Machine Learning
(18)
Optimization & Theory
(14)
Mathematics & Optimization
(8)
Optimization
(8)
Artificial Intelligence
(7)
Application Areas
(7)
Learning Paradigms · Artificial Intelligence
(6)
Learning Types
(5)
Learning Paradigms · Machine Learning
(4)
Computer Science
(2)
Deep Learning
(2)
Systems
(2)
Security & Privacy
(1)
Bayesian & Probabilistic
(1)
Core AI
(1)
Keywords
federated learning
(8)
differential privacy
(6)
stochastic gradient descent
(4)
online learning
(3)
regret bound
(3)
distributed learning
(3)
convex optimization
(3)
communication efficiency
(2)
unconstrained optimization
(2)
online convex optimization
(2)
learning rate
(2)
distributed optimization
(2)
minimax optimality
(1)
sample complexity
(1)
randomized rounding
(1)
supervised learning
(1)
gaussian process
(1)
game theory
(1)
active learning
(1)
regret minimization
(1)
Papers
Federated Learning of Gboard Language Models with Differential Privacy
ACL 2023
Differentially Private Learning with Adaptive Clipping
NeurIPS 2021
Practical and Private (Deep) Learning Without Sampling or Shuffling
ICML 2021
Federated Heavy Hitters Discovery with Differential Privacy
AISTATS 2020
Is Local SGD Better than Minibatch SGD?
ICML 2020
Privacy Amplification via Random Check-Ins
NeurIPS 2020
Semi-Cyclic Stochastic Gradient Descent
ICML 2019
cpSGD: Communication-efficient and differentially-private distributed SGD
NeurIPS 2018
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
NeurIPS 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data
AISTATS 2017
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
NeurIPS 2014
Estimation, Optimization, and Parallelism when Data is Sparse
NeurIPS 2013
Large-Scale Learning with Less RAM via Randomization
ICML 2013
Minimax Optimal Algorithms for Unconstrained Linear Optimization
NeurIPS 2013
No-Regret Algorithms for Unconstrained Online Convex Optimization
NeurIPS 2012
Discussion of “Contextual Bandit Algorithms with Supervised Learning Guarantees”
AISTATS 2011
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization
AISTATS 2011
Selecting Observations against Adversarial Objectives
NeurIPS 2007