H. Brendan McMahan

According to our database, H. Brendan McMahan authored at least 90 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Federated Learning in Practice: Reflections and Projections.

Katharine Daly, Hubert Eichner, Peter Kairouz, H. Brendan McMahan, Daniel Ramage, Zheng Xu

CoRR, 2024

A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs.

H. Brendan McMahan, Zheng Xu, Yanxiang Zhang

CoRR, 2024

Fine-Tuning Large Language Models with User-Level Differential Privacy.

Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush

CoRR, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

CoRR, 2024

Confidential Federated Computations.

Hubert Eichner, Daniel Ramage, Kallista A. Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adrià Gascón, Marco Gruteser, Brendan McMahan

CoRR, 2024

One-shot Empirical Privacy Estimation for Federated Learning.

Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

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

J. Artif. Intell. Res., 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

CoRR, 2023

Can Public Large Language Models Help Private Cross-device Federated Learning?

Boxin Wang, Jacky Yibo Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

CoRR, 2023

An Empirical Evaluation of Federated Contextual Bandit Algorithms.

Alekh Agarwal, H. Brendan McMahan, Zheng Xu

CoRR, 2023

Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan

CoRR, 2023

Unleashing the Power of Randomization in Auditing Differentially Private ML.

Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy.

Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy.

Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning.

Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta

Proceedings of the International Conference on Machine Learning, 2023

Differentially Private Adaptive Optimization with Delayed Preconditioners.

Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning to Generate Image Embeddings with User-Level Differential Privacy.

Zheng Xu, Maxwell D. Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Federated Learning of Gboard Language Models with Differential Privacy.

Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A. Choquette-Choo, Peter Kairouz, H. Brendan McMahan, Jesse Rosenstock, Yuanbo Zhang

Proceedings of the The 61st Annual Meeting of the Association for Computational Linguistics: Industry Track, 2023

2022

Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning.

Zachary Charles, Kallista A. Bonawitz, Stanislav Chiknavaryan, Brendan McMahan, Blaise Agüera y Arcas

CoRR, 2022

Private Online Prefix Sums via Optimal Matrix Factorizations.

Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta

CoRR, 2022

Federated learning and privacy.

Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage

Commun. ACM, 2022

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams.

Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Federated Learning and Privacy: Building privacy-preserving systems for machine learning and data science on decentralized data.

Kallista A. Bonawitz, Peter Kairouz, Brendan McMahan, Daniel Ramage

ACM Queue, 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

Found. Trends Mach. Learn., 2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Differentially Private Learning with Adaptive Clipping.

Galen Andrew, Om Thakkar, Brendan McMahan, Swaroop Ramaswamy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.

Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Proceedings of the 38th International Conference on Machine Learning, 2021

Adaptive Federated Optimization.

Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Training Production Language Models without Memorizing User Data.

Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, Françoise Beaufays

CoRR, 2020

Privacy Amplification via Random Check-Ins.

Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, Abhradeep Thakurta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Is Local SGD Better than Minibatch SGD?

Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 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 Agüera y Arcas

Proceedings of the 8th International Conference on Learning Representations, 2020

Federated Heavy Hitters Discovery with Differential Privacy.

Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

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, 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

CoRR, 2019

Can You Really Backdoor Federated Learning?

Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan

CoRR, 2019

Differentially Private Learning with Adaptive Clipping.

Om Thakkar, Galen Andrew, H. Brendan McMahan

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

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 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 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

CoRR, 2019

Towards Federated Learning at Scale: System Design.

Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Semi-Cyclic Stochastic Gradient Descent.

Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar

Proceedings of the 36th International Conference on Machine Learning, 2019

Federated Learning with Autotuned Communication-Efficient Secure Aggregation.

Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar

CoRR, 2018

A General Approach to Adding Differential Privacy to Iterative Training Procedures.

H. Brendan McMahan, Galen Andrew

CoRR, 2018

LEAF: A Benchmark for Federated Settings.

Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

CoRR, 2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.

Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

CoRR, 2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.

Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

cpSGD: Communication-efficient and differentially-private distributed SGD.

Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Differentially Private Recurrent Language Models.

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

A survey of Algorithms and Analysis for Adaptive Online Learning.

H. Brendan McMahan

J. Mach. Learn. Res., 2017

Practical Secure Aggregation for Privacy Preserving Machine Learning.

Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

IACR Cryptol. ePrint Arch., 2017

Learning Differentially Private Language Models Without Losing Accuracy.

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

CoRR, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches.

Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang

CoRR, 2017

Distributed Mean Estimation with Limited Communication.

Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

Proceedings of the 34th International Conference on Machine Learning, 2017

On the Protection of Private Information in Machine Learning Systems: Two Recent Approches.

Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang

Proceedings of the 30th IEEE Computer Security Foundations Symposium, 2017

Communication-Efficient Learning of Deep Networks from Decentralized Data.

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Federated Learning of Deep Networks using Model Averaging.

H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Agüera y Arcas

CoRR, 2016

Federated Learning: Strategies for Improving Communication Efficiency.

Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon

CoRR, 2016

Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik

CoRR, 2016

Practical Secure Aggregation for Federated Learning on User-Held Data.

Kallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

CoRR, 2016

Deep Learning with Differential Privacy.

Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang

Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016

2015

Federated Optimization: Distributed Optimization Beyond the Datacenter.

Jakub Konecný, Brendan McMahan, Daniel Ramage

CoRR, 2015

2014

Analysis Techniques for Adaptive Online Learning.

H. Brendan McMahan

CoRR, 2014

Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning.

H. Brendan McMahan, Matthew J. Streeter

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations.

H. Brendan McMahan, Francesco Orabona

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Minimax Optimal Algorithms for Unconstrained Linear Optimization

H. Brendan McMahan

CoRR, 2013

Minimax Optimal Algorithms for Unconstrained Linear Optimization.

H. Brendan McMahan, Jacob D. Abernethy

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Estimation, Optimization, and Parallelism when Data is Sparse.

John C. Duchi, Michael I. Jordan, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Ad click prediction: a view from the trenches.

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

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Large-Scale Learning with Less RAM via Randomization.

Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Open Problem: Better Bounds for Online Logistic Regression.

H. Brendan McMahan, Matthew J. Streeter

Proceedings of the COLT 2012, 2012

On Calibrated Predictions for Auction Selection Mechanisms

H. Brendan McMahan, Omkar Muralidharan

CoRR, 2012

No-Regret Algorithms for Unconstrained Online Convex Optimization.

Matthew J. Streeter, H. Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization.

H. Brendan McMahan

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees".

H. Brendan McMahan

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010

Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and Implicit Updates

H. Brendan McMahan

CoRR, 2010

Less Regret via Online Conditioning

Matthew J. Streeter, H. Brendan McMahan

CoRR, 2010

Adaptive Bound Optimization for Online Convex Optimization.

H. Brendan McMahan, Matthew J. Streeter

Proceedings of the COLT 2010, 2010

2009

Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards.

Varun Kanade, H. Brendan McMahan, Brent Bryan

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Tighter Bounds for Multi-Armed Bandits with Expert Advice.

H. Brendan McMahan, Matthew J. Streeter

Proceedings of the COLT 2009, 2009

2007

A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games.

H. Brendan McMahan, Geoffrey J. Gordon

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Selecting Observations against Adversarial Objectives.

Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Efficiently computing minimax expected-size confidence regions.

Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider

Proceedings of the Machine Learning, 2007

A Unification of Extensive-Form Games and Markov Decision Processes.

H. Brendan McMahan, Geoffrey J. Gordon

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2005

Online convex optimization in the bandit setting: gradient descent without a gradient.

Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan

Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2005

Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees.

H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon

Proceedings of the Machine Learning, 2005

Fast Exact Planning in Markov Decision Processes.

H. Brendan McMahan, Geoffrey J. Gordon

Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling (ICAPS 2005), 2005

2004

Multi-source spanning trees: algorithms for minimizing source eccentricities.

H. Brendan McMahan, Andrzej Proskurowski

Discret. Appl. Math., 2004

Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary.

H. Brendan McMahan, Avrim Blum

Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003

Planning in the Presence of Cost Functions Controlled by an Adversary.

H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum

Proceedings of the Machine Learning, 2003