HM
H. Brendan McMahan
cs.LGcs.CRstat.MLcs.AIcs.DCcs.DSmath.OCcs.CCcs.CLcs.CV
On Valency
published · living versionsW_q85w3jp3·v1 · currentpublished
Preprints & journals
47 papers in the corpus · 2010–2025How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy2512.03238v2 · Natalia Ponomareva, Zheng Xu, H. Brendan McMahan et al.2025 · 1 citationJAIR 2026, vol. 86 JAIR, Vol. 86
Correlated Noise Mechanisms for Differentially Private Learning2506.08201v1 · Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo et al.2025 · 2 citationsarXiv
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs2408.08868v3 · H. Brendan McMahan, Zheng Xu, Yanxiang Zhang2024 · 1 citationarXiv
An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy2504.21413v1 · H. Brendan McMahan, Krishna Pillutla2025 · 0 citationsarXiv
Federated Learning in Practice: Reflections and Projections2410.08892v2 · Katharine Daly, Hubert Eichner, Peter Kairouz et al.2024 · 17 citationsarXiv
Fine-Tuning Large Language Models with User-Level Differential Privacy2407.07737v1 · Zachary Charles, Arun Ganesh, Ryan McKenna et al.2024 · 4 citationsarXiv
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy2404.16706v3 · Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla et al.2024 · 7 citationsarXiv
One-shot Empirical Privacy Estimation for Federated Learning2302.03098v5 · Galen Andrew, Peter Kairouz, Sewoong Oh et al.2023 · 5 citationsarXiv
Can Public Large Language Models Help Private Cross-device Federated Learning?2305.12132v2 · Boxin Wang, Yibo Jacky Zhang, Yuan Cao et al.2023 · 14 citationsarXiv
(Amplified) Banded Matrix Factorization: A unified approach to private training2306.08153v2 · Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna et al.2023 · 2 citationsarXiv
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy2303.00654v3 · Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin et al.2023 · 161 citationsJournal of Artificial Intelligence Research 77 (2023) 1113-1201
Federated Learning of Gboard Language Models with Differential Privacy2305.18465v2 · Zheng Xu, Yanxiang Zhang, Galen Andrew et al.2023 · 55 citationsarXiv
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning2211.06530v2 · Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush et al.2022 · 6 citationsarXiv
Differentially Private Adaptive Optimization with Delayed Preconditioners2212.00309v2 · Tian Li, Manzil Zaheer, Ken Ziyu Liu et al.2022 · 3 citationsarXiv
Unleashing the Power of Randomization in Auditing Differentially Private ML2305.18447v1 · Krishna Pillutla, Galen Andrew, Peter Kairouz et al.2023 · 6 citationsarXiv
Learning to Generate Image Embeddings with User-level Differential Privacy2211.10844v2 · Zheng Xu, Maxwell Collins, Yuxiao Wang et al.2022 · 20 citationsarXiv
An Empirical Evaluation of Federated Contextual Bandit Algorithms2303.10218v1 · Alekh Agarwal, H. Brendan McMahan, Zheng Xu2023 · 0 citationsarXiv
Differentially Private Learning with Adaptive Clipping1905.03871v5 · Galen Andrew, Om Thakkar, H. Brendan McMahan et al.2019 · 131 citationsarXiv
Adaptive Federated Optimization2003.00295v5 · Sashank Reddi, Zachary Charles, Manzil Zaheer et al.2020 · 126 citationsarXiv
A Field Guide to Federated Optimization2107.06917v1 · Jianyu Wang, Zachary Charles, Zheng Xu et al.2021 · 167 citationsarXiv
Advances and Open Problems in Federated Learning1912.04977v3 · Peter Kairouz, H. Brendan McMahan, Brendan Avent et al.2019 · 6,306 citationsarXiv
Training Production Language Models without Memorizing User Data2009.10031v1 · Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews et al.2020 · 15 citationsarXiv
Privacy Amplification via Random Check-Ins2007.06605v2 · Borja Balle, Peter Kairouz, H. Brendan McMahan et al.2020 · 2 citationsarXiv
Is Local SGD Better than Minibatch SGD?2002.07839v2 · Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich et al.2020 · 104 citationsarXiv
Generative Models for Effective ML on Private, Decentralized Datasets1911.06679v2 · Sean Augenstein, H. Brendan McMahan, Daniel Ramage et al.2019 · 43 citationsarXiv
LEAF: A Benchmark for Federated Settings1812.01097v3 · Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu et al.2018 · 281 citationsarXiv
MLSys: The New Frontier of Machine Learning Systems1904.03257v3 · Alexander Ratner, Dan Alistarh, Gustavo Alonso et al.2019 · 20 citationsarXivon Valency
Semi-Cyclic Stochastic Gradient Descent1904.10120v1 · Hubert Eichner, Tomer Koren, H. Brendan McMahan et al.2019 · 18 citationsarXiv
Towards Federated Learning at Scale: System Design1902.01046v2 · Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp et al.2019 · 938 citationsarXiv
A General Approach to Adding Differential Privacy to Iterative Training Procedures1812.06210v2 · H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson et al.2018 · 123 citationsarXiv
Deep Learning with Differential Privacy1607.00133v2 · Mart'in Abadi, Andy Chu, Ian Goodfellow et al.2016 · 6,377 citationsProceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (ACM CCS), pp. 308-318, 2016on Valency
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches1708.08022v1 · Mart'in Abadi, 'Ulfar Erlingsson, Ian Goodfellow et al.2017 · 41 citationsIEEE 30th Computer Security Foundations Symposium (CSF), pages 1--6, 2017
cpSGD: Communication-efficient and differentially-private distributed SGD1805.10559v1 · Naman Agarwal, Ananda Theertha Suresh, Felix Yu et al.2018 · 310 citationsarXiv
Learning Differentially Private Recurrent Language Models1710.06963v3 · H. Brendan McMahan, Daniel Ramage, Kunal Talwar et al.2017 · 766 citationsarXiv
Federated Learning: Strategies for Improving Communication Efficiency1610.05492v2 · Jakub Konecn'y, H. Brendan McMahan, Felix X. Yu et al.2016 · 3,032 citationsarXiv
Distributed Mean Estimation with Limited Communication1611.00429v3 · Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar et al.2016 · 145 citationsarXiv
Practical Secure Aggregation for Federated Learning on User-Held Data1611.04482v1 · Keith Bonawitz, Vladimir Ivanov, Ben Kreuter et al.2016 · 187 citationsarXiv
Federated Optimization: Distributed Machine Learning for On-Device Intelligence1610.02527v1 · Jakub Konecn'y, H. Brendan McMahan, Daniel Ramage et al.2016 · 1,627 citationsarXivon Valency
A Survey of Algorithms and Analysis for Adaptive Online Learning1403.3465v3 · H. Brendan McMahan2014 · 113 citationsarXiv
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations1403.0628v2 · H. Brendan McMahan, Francesco Orabona2014 · 36 citationsarXivon Valency
Large-Scale Learning with Less RAM via Randomization1303.4664v1 · Daniel Golovin, D. Sculley, H. Brendan McMahan et al.2013 · 18 citationsarXiv
Minimax Optimal Algorithms for Unconstrained Linear Optimization1302.2176v1 · H. Brendan McMahan2013 · 1 citationarXiv
On Calibrated Predictions for Auction Selection Mechanisms1211.3955v1 · H. Brendan McMahan, Omkar Muralidharan2012 · 1 citationarXiv
No-Regret Algorithms for Unconstrained Online Convex Optimization1211.2260v1 · Matthew Streeter, H. Brendan McMahan2012 · 30 citationsNIPS 2012on Valency
A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates1009.3240v2 · H. Brendan McMahan2010 · 24 citationsarXiv
Adaptive Bound Optimization for Online Convex Optimization1002.4908v2 · H. Brendan McMahan, Matthew Streeter2010 · 133 citationsProceedings of the 23rd Annual Conference on Learning Theory (COLT) 2010on Valency
Less Regret via Online Conditioning1002.4862v1 · Matthew Streeter, H. Brendan McMahan2010 · 22 citationsarXivon Valency
Career total: 2 works. 47 are in this corpus.
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