H. Brendan McMahan

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 53

Top Topics

Contextual Bandit

Differential Privacy

Top Venues

Publications

Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR (2024)
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR (2024)
Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
Can Public Large Language Models Help Private Cross-device Federated Learning? CoRR (2023)
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
Federated Learning of Gboard Language Models with Differential Privacy. CoRR (2023)
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith Suriyakumar
One-shot Empirical Privacy Estimation for Federated Learning. CoRR (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS (2023)
Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR (2023)
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR (2023)
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS (2023)
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. 77 (2023)
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR (2023)
Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS (2023)
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) (2023)
Alekh Agarwal, H. Brendan McMahan, Zheng Xu
An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR (2023)
Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. NeurIPS (2022)
Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR (2022)
Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR (2022)
Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. CoRR (2022)
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
A Field Guide to Federated Optimization. CoRR (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
Privacy Amplification via Random Check-Ins. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? ICML (2020)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan
Adaptive Federated Optimization. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? CoRR (2020)
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays
Training Production Language Models without Memorizing User Data. CoRR (2020)
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
Generative Models for Effective ML on Private, Decentralized Datasets. ICLR (2020)
Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
Semi-Cyclic Stochastic Gradient Descent. CoRR (2019)
Om Thakkar, Galen Andrew, H. Brendan McMahan
Differentially Private Learning with Adaptive Clipping. CoRR (2019)
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
Can You Really Backdoor Federated Learning? CoRR (2019)
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
Generative Models for Effective ML on Private, Decentralized Datasets. CoRR (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Towards Federated Learning at Scale: System Design. CoRR (2019)
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
SysML: The New Frontier of Machine Learning Systems. CoRR (2019)
Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. CoRR (2018)
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
LEAF: A Benchmark for Federated Settings. CoRR (2018)
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR (2018)
H. Brendan McMahan, Galen Andrew
A General Approach to Adding Differential Privacy to Iterative Training Procedures. CoRR (2018)
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
Learning Differentially Private Recurrent Language Models. ICLR (Poster) (2018)
Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
Practical Secure Aggregation for Privacy-Preserving Machine Learning. CCS (2017)
Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF (2017)
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
Distributed Mean Estimation with Limited Communication. ICML (2017)
Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
Practical Secure Aggregation for Privacy Preserving Machine Learning. IACR Cryptol. ePrint Arch. 2017 (2017)
H. Brendan McMahan
A survey of Algorithms and Analysis for Adaptive Online Learning. J. Mach. Learn. Res. 18 (2017)
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
Learning Differentially Private Language Models Without Losing Accuracy. CoRR (2017)
Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR (2017)
Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
Practical Secure Aggregation for Federated Learning on User-Held Data. CoRR (2016)
Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR (2016)
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Deep Learning with Differential Privacy. CoRR (2016)
H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas
Federated Learning of Deep Networks using Model Averaging. CoRR (2016)
Ananda Theertha Suresh, Felix X. Yu, H. Brendan McMahan, Sanjiv Kumar
Distributed Mean Estimation with Limited Communication. CoRR (2016)
Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon
Federated Learning: Strategies for Improving Communication Efficiency. CoRR (2016)
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Deep Learning with Differential Privacy. CCS (2016)
H. Brendan McMahan, Francesco Orabona
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. CoRR (2014)
H. Brendan McMahan, Francesco Orabona
Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. COLT (2014)
H. Brendan McMahan
Analysis Techniques for Adaptive Online Learning. CoRR (2014)
H. Brendan McMahan, Matthew J. Streeter
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning. NIPS (2014)
H. Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica
Ad click prediction: a view from the trenches. KDD (2013)
John C. Duchi, Michael I. Jordan, H. Brendan McMahan
Estimation, Optimization, and Parallelism when Data is Sparse. NIPS (2013)
H. Brendan McMahan
Minimax Optimal Algorithms for Unconstrained Linear Optimization CoRR (2013)
H. Brendan McMahan, Jacob D. Abernethy
Minimax Optimal Algorithms for Unconstrained Linear Optimization. NIPS (2013)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization CoRR (2013)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization. ICML (2) (2013)
H. Brendan McMahan, Omkar Muralidharan
On Calibrated Predictions for Auction Selection Mechanisms CoRR (2012)
Matthew J. Streeter, H. Brendan McMahan
No-Regret Algorithms for Unconstrained Online Convex Optimization. NIPS (2012)
H. Brendan McMahan, Matthew J. Streeter
Open Problem: Better Bounds for Online Logistic Regression. COLT (2012)
Matthew J. Streeter, H. Brendan McMahan
No-Regret Algorithms for Unconstrained Online Convex Optimization CoRR (2012)
H. Brendan McMahan
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization. AISTATS (2011)
H. Brendan McMahan
Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees". AISTATS (2011)
H. Brendan McMahan, Matthew J. Streeter
Adaptive Bound Optimization for Online Convex Optimization. COLT (2010)
H. Brendan McMahan
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and Implicit Updates CoRR (2010)
Matthew J. Streeter, H. Brendan McMahan
Less Regret via Online Conditioning CoRR (2010)
H. Brendan McMahan, Matthew J. Streeter
Adaptive Bound Optimization for Online Convex Optimization CoRR (2010)
Varun Kanade, H. Brendan McMahan, Brent Bryan
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. AISTATS (2009)
H. Brendan McMahan, Matthew J. Streeter
Tighter Bounds for Multi-Armed Bandits with Expert Advice. COLT (2009)
Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider
Efficiently computing minimax expected-size confidence regions. ICML (2007)
H. Brendan McMahan, Geoffrey J. Gordon
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games. AISTATS (2007)
Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta
Selecting Observations against Adversarial Objectives. NIPS (2007)
H. Brendan McMahan, Geoffrey J. Gordon
A Unification of Extensive-Form Games and Markov Decision Processes. AAAI (2007)
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan
Online convex optimization in the bandit setting: gradient descent without a gradient. SODA (2005)
H. Brendan McMahan, Geoffrey J. Gordon
Fast Exact Planning in Markov Decision Processes. ICAPS (2005)
H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. ICML (2005)
H. Brendan McMahan, Andrzej Proskurowski
Multi-source spanning trees: algorithms for minimizing source eccentricities. Discret. Appl. Math. 137 (2) (2004)
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan
Online convex optimization in the bandit setting: gradient descent without a gradient CoRR (2004)
H. Brendan McMahan, Avrim Blum
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. COLT (2004)
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum
Planning in the Presence of Cost Functions Controlled by an Adversary. ICML (2003)