H. Brendan McMahan

Google Publications

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

    H. Brendan McMahan, Francesco Orabona

    Proceedings of the 27th Annual Conference on Learning Theory (COLT) (2014) (to appear)

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    Ad Click Prediction: a View from the Trenches

    H. Brendan McMahan, Gary Holt, D. 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 (KDD) (2013)

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    Estimation, Optimization, and Parallelism when Data is Sparse

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

    Advances in Neural Information Processing Systems (NIPS) (2013)

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    Large-Scale Learning with Less RAM via Randomization

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

    Proceedings of the 30 International Conference on Machine Learning (ICML) (2013), pp. 10

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    Minimax Optimal Algorithms for Unconstrained Linear Optimization

    H. Brendan McMahan, Jacob Abernethy

    Advances in Neural Information Processing Systems (NIPS) (2013)

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    No-Regret Algorithms for Unconstrained Online Convex Optimization

    Matthew Streeter, H. Brendan McMahan

    Advances in Neural Information Processing Systems (NIPS) (2012)

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    Open Problem: Better Bounds for Online Logistic Regression

    H. Brendan McMahan, Matthew Streeter

    COLT/ICML Joint Open Problem Session, JMLR: Workshop and Conference Proceedings (2012)

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

    H. Brendan McMahan

    Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) (2011)

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    Adaptive Bound Optimization for Online Convex Optimization

    H. Brendan McMahan, Matthew Streeter

    Proceedings of the 23rd Annual Conference on Learning Theory (COLT) (2010)

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    Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards

    Varun Kanade, H. Brendan McMahan, Brent Bryan

    Proceedings of the 12th International Conference on Artificial Intelligence and Statistic (AISTATS) (2009)

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    Tighter Bounds for Multi-Armed Bandits with Expert Advice

    H. Brendan McMahan, Matthew Streeter

    Proceedings of the 22nd Annual Conference on Learning Theory (COLT) (2009)

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    Robust Submodular Observation Selection

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

    Journal of Machine Learning Research (JMLR), vol. 9 (2008), pp. 2761-2801

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    Efficiently Computing Minimax Expected-Size Confidence Regions

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

    Proc. 24th ICML, ACM, Corvalis (2007), pp. 97-104

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    Selecting Observations Against Adversarial Objectives

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

    Advances in Neural Information Processing Systems (NIPS 2007)

Previous Publications

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

    H. Brendan McMahan, Geoffrey J. Gordon

    Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS) (2007)

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    A Unification of Extensive-Form Games and Markov Decision Processes

    H. Brendan McMahan, Geoffrey J. Gordon

    AAAI 2007

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    Robust Planning in Domains with Stochastic Outcomes, Adversaries, and Partial Observability

    H. Brendan McMahan

    CMU (2006)

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    Bounded Real-Time Dynamic Programming: RTDP with monotone upper bounds and performance guarantees

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

    Proceedings of the 22nd International Conference on Machine Learning (ICML) (2005)

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    Fast Exact Planning in Markov Decision Processes

    H. Brendan McMahan, Geoffrey J. Gordon

    International Conference on Automated Planning and Scheduling (ICAPS) (2005)

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    Generalizing Dijkstra's algorithm and Gaussian Elimination for solving MDPs

    H. Brendan McMahan, Geoffrey J. Gordon

    Carnegie Mellon University (2005)

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    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 (SODA) (2005)

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    Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary

    H. Brendan McMahan, Avrim Blum

    Proceedings of the Seventeenth Annual Conference on Learning Theory (COLT) (2004), pp. 109-123

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    Multi-source spanning trees: algorithms for minimizing source eccentricities

    H. Brendan McMahan, A. Proskurowski

    Discrete Applied Mathematics, vol. 137/2 (2003), pp. 213-222

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    Planning in the Presence of Cost Functions Controlled By An Adversary

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

    In Proceedings of the 20th International Conference on Machine Learning (ICML) (2003)