H. Brendan McMahan

94 papers A* 23A 5Journal 56Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy. CoRR H. Brendan McMahan, Krishna Pillutla
2025	J	`jnl`	Correlated Noise Mechanisms for Differentially Private Learning. CoRR Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Arun Ganesh, Monika Henzinger, Jonathan Katz, Ryan McKenna, H. Brendan McMahan, Keith Rush, Thomas Steinke, Abhradeep Thakurta
2025	J	`jnl`	How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy. CoRR Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
2025	—	`conf`	Learning with User-Level Differential Privacy Under Fixed Compute Budgets. SaTML Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2024	J	`jnl`	A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs. CoRR H. Brendan McMahan, Zheng Xu, Yanxiang Zhang
2024	A*	`conf`	Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. FOCS Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
2024	J	`jnl`	Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
2024	—	`conf`	Federated Learning in Practice: Reflections and Projections. TPS-ISA Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Federated Learning in Practice: Reflections and Projections. CoRR Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu
2024	J	`jnl`	Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2023	A*	`conf`	(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR Alekh Agarwal, H. Brendan McMahan, Zheng Xu
2023	J	`jnl`	Can Public Large Language Models Help Private Cross-device Federated Learning? CoRR Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
2023	—	`conf`	Federated Learning of Gboard Language Models with Differential Privacy. ACL (industry) Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	J	`jnl`	Federated Learning of Gboard Language Models with Differential Privacy. CoRR Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang
2023	A*	`conf`	Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
2023	J	`jnl`	How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. CoRR Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
2023	J	`jnl`	How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. J. Artif. Intell. Res. Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta
2023	A*	`conf`	Learning to Generate Image Embeddings with User-Level Differential Privacy. CVPR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2023	J	`jnl`	One-shot Empirical Privacy Estimation for Federated Learning. CoRR Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith M. Suriyakumar
2023	A*	`conf`	Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2023	J	`jnl`	Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2022	J	`jnl`	Differentially Private Adaptive Optimization with Delayed Preconditioners. CoRR Tian Li, Manzil Zaheer, Ken Ziyu Liu, Sashank J. Reddi, H. Brendan McMahan, Virginia Smith
2022	A*	`conf`	Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. NeurIPS Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta
2022	J	`jnl`	Learning to Generate Image Embeddings with User-level Differential Privacy. CoRR Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan
2022	J	`jnl`	Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. CoRR Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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 Horváth, 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
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2020	J	`jnl`	Adaptive Federated Optimization. CoRR Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan
2020	A*	`conf`	Generative Models for Effective ML on Private, Decentralized Datasets. ICLR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2020	A*	`conf`	Is Local SGD Better than Minibatch SGD? ICML Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Is Local SGD Better than Minibatch SGD? CoRR Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Privacy Amplification via Random Check-Ins. CoRR Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, Abhradeep Thakurta
2020	J	`jnl`	Training Production Language Models without Memorizing User Data. CoRR Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Can You Really Backdoor Federated Learning? CoRR Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
2019	J	`jnl`	Differentially Private Learning with Adaptive Clipping. CoRR Om Thakkar, Galen Andrew, H. Brendan McMahan
2019	J	`jnl`	Generative Models for Effective ML on Private, Decentralized Datasets. CoRR Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Agüera y Arcas
2019	J	`jnl`	Semi-Cyclic Stochastic Gradient Descent. CoRR Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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 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 Randall 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
2019	J	`jnl`	Towards Federated Learning at Scale: System Design. CoRR 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
2018	J	`jnl`	A General Approach to Adding Differential Privacy to Iterative Training Procedures. CoRR H. Brendan McMahan, Galen Andrew
2018	J	`jnl`	Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. CoRR Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar
2018	J	`jnl`	LEAF: A Benchmark for Federated Settings. CoRR Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
2018	—	`conf`	Learning Differentially Private Recurrent Language Models. ICLR (Poster) H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2018	J	`jnl`	cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
2017	J	`jnl`	A survey of Algorithms and Analysis for Adaptive Online Learning. J. Mach. Learn. Res. H. Brendan McMahan
2017	A*	`conf`	Distributed Mean Estimation with Limited Communication. ICML Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
2017	J	`jnl`	Learning Differentially Private Language Models Without Losing Accuracy. CoRR H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
2017	J	`jnl`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	A	`conf`	On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
2017	J	`jnl`	Practical Secure Aggregation for Privacy Preserving Machine Learning. IACR Cryptol. ePrint Arch. Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2017	A*	`conf`	Practical Secure Aggregation for Privacy-Preserving Machine Learning. CCS Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2016	A*	`conf`	Deep Learning with Differential Privacy. CCS Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
2016	J	`jnl`	Deep Learning with Differential Privacy. CoRR Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
2016	J	`jnl`	Distributed Mean Estimation with Limited Communication. CoRR Ananda Theertha Suresh, Felix X. Yu, H. Brendan McMahan, Sanjiv Kumar
2016	J	`jnl`	Federated Learning of Deep Networks using Model Averaging. CoRR H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas
2016	J	`jnl`	Federated Learning: Strategies for Improving Communication Efficiency. CoRR Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon
2016	J	`jnl`	Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
2016	J	`jnl`	Practical Secure Aggregation for Federated Learning on User-Held Data. CoRR Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
2014	J	`jnl`	Analysis Techniques for Adaptive Online Learning. CoRR H. Brendan McMahan
2014	—	`conf`	Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning. NIPS H. Brendan McMahan, Matthew J. Streeter
2014	A*	`conf`	Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. COLT H. Brendan McMahan, Francesco Orabona
2014	J	`jnl`	Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations. CoRR H. Brendan McMahan, Francesco Orabona
2013	A*	`conf`	Ad click prediction: a view from the trenches. KDD 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
2013	—	`conf`	Estimation, Optimization, and Parallelism when Data is Sparse. NIPS John C. Duchi, Michael I. Jordan, H. Brendan McMahan
2013	J	`jnl`	Large-Scale Learning with Less RAM via Randomization CoRR Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
2013	—	`conf`	Large-Scale Learning with Less RAM via Randomization. ICML (2) Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
2013	J	`jnl`	Minimax Optimal Algorithms for Unconstrained Linear Optimization CoRR H. Brendan McMahan
2013	—	`conf`	Minimax Optimal Algorithms for Unconstrained Linear Optimization. NIPS H. Brendan McMahan, Jacob D. Abernethy
2012	J	`jnl`	No-Regret Algorithms for Unconstrained Online Convex Optimization CoRR Matthew J. Streeter, H. Brendan McMahan
2012	—	`conf`	No-Regret Algorithms for Unconstrained Online Convex Optimization. NIPS Matthew J. Streeter, H. Brendan McMahan
2012	J	`jnl`	On Calibrated Predictions for Auction Selection Mechanisms CoRR H. Brendan McMahan, Omkar Muralidharan
2012	A*	`conf`	Open Problem: Better Bounds for Online Logistic Regression. COLT H. Brendan McMahan, Matthew J. Streeter
2011	A	`conf`	Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees". AISTATS H. Brendan McMahan
2011	A	`conf`	Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization. AISTATS H. Brendan McMahan
2010	J	`jnl`	Adaptive Bound Optimization for Online Convex Optimization CoRR H. Brendan McMahan, Matthew J. Streeter
2010	A*	`conf`	Adaptive Bound Optimization for Online Convex Optimization. COLT H. Brendan McMahan, Matthew J. Streeter
2010	J	`jnl`	Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and Implicit Updates CoRR H. Brendan McMahan
2010	J	`jnl`	Less Regret via Online Conditioning CoRR Matthew J. Streeter, H. Brendan McMahan
2009	A	`conf`	Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. AISTATS Varun Kanade, H. Brendan McMahan, Brent Bryan
2009	A*	`conf`	Tighter Bounds for Multi-Armed Bandits with Expert Advice. COLT H. Brendan McMahan, Matthew J. Streeter
2007	A	`conf`	A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games. AISTATS H. Brendan McMahan, Geoffrey J. Gordon
2007	A*	`conf`	A Unification of Extensive-Form Games and Markov Decision Processes. AAAI H. Brendan McMahan, Geoffrey J. Gordon
2007	A*	`conf`	Efficiently computing minimax expected-size confidence regions. ICML Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider
2007	—	`conf`	Selecting Observations against Adversarial Objectives. NIPS Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta
2005	A*	`conf`	Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. ICML H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon
2005	A*	`conf`	Fast Exact Planning in Markov Decision Processes. ICAPS H. Brendan McMahan, Geoffrey J. Gordon
2005	A*	`conf`	Online convex optimization in the bandit setting: gradient descent without a gradient. SODA Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan
2004	J	`jnl`	Multi-source spanning trees: algorithms for minimizing source eccentricities. Discret. Appl. Math. H. Brendan McMahan, Andrzej Proskurowski
2004	A*	`conf`	Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. COLT H. Brendan McMahan, Avrim Blum
2004	J	`jnl`	Online convex optimization in the bandit setting: gradient descent without a gradient CoRR Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan
2003	A*	`conf`	Planning in the Presence of Cost Functions Controlled by an Adversary. ICML H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum