McMahan, H. Brendan

23 publications

NeurIPS 2023 (Amplified) Banded Matrix Factorization: A Unified Approach to Private Training Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu

NeurIPS 2023 Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasiia Koloskova, Ryan McKenna, Zachary Charles, John Rush, H. Brendan McMahan

JAIR 2023 How to DP-Fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

CVPR 2023 Learning to Generate Image Embeddings with User-Level Differential Privacy Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

NeurIPS 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

NeurIPS 2022 Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams Sergey Denisov, H. Brendan McMahan, John Rush, Adam Smith, Abhradeep Guha Thakurta

FnTML 2021 Advances and Open Problems in Federated Learning 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

ICLR 2020 Generative Models for Effective ML on Private, Decentralized Datasets Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

ICLR 2018 Learning Differentially Private Recurrent Language Models H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

JMLR 2017 A Survey of Algorithms and Analysis for Adaptive Online Learning H. Brendan McMahan

ICML 2017 Distributed Mean Estimation with Limited Communication Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

COLT 2014 Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations H. Brendan McMahan, Francesco Orabona

COLT 2012 Open Problem: Better Bounds for Online Logistic Regression H. Brendan McMahan, Matthew Streeter

COLT 2010 Adaptive Bound Optimization for Online Convex Optimization H. Brendan McMahan, Matthew J. Streeter

AISTATS 2009 Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards Varun Kanade, H. Brendan McMahan, Brent Bryan

COLT 2009 Tighter Bounds for Multi-Armed Bandits with Expert Advice H. Brendan McMahan, Matthew J. Streeter

JMLR 2008 Robust Submodular Observation Selection Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta

AISTATS 2007 A Fast Bundle-Based Anytime Algorithm for Poker and Other Convex Games H. Brendan McMahan, Geoffrey J. Gordon

AAAI 2007 A Unification of Extensive-Form Games and Markov Decision Processes H. Brendan McMahan, Geoffrey J. Gordon

ICML 2007 Efficiently Computing Minimax Expected-Size Confidence Regions Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider

ICML 2005 Bounded Real-Time Dynamic Programming: RTDP with Monotone Upper Bounds and Performance Guarantees H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon

COLT 2004 Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary H. Brendan McMahan, Avrim Blum

ICML 2003 Planning in the Presence of Cost Functions Controlled by an Adversary H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum