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Google Research
Other Google Resources
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Efficiently Computing Minimax Expected-Size Confidence Regions, Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff Schneider, Proc. 24th ICML, 2007, pp. 97-104.
Selecting Observations Against Adversarial Objectives, Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta, Advances in Neural Information Processing Systems (NIPS 2007).
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.
A Unification of Extensive-Form Games and Markov Decision Processes, H. Brendan McMahan, Geoffrey J. Gordon, Submitted to AAAI 2007.
Robust Planning in Domains with Stochastic Outcomes, Adversaries, and Partial Observability, H. Brendan McMahan, 2006.
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.
Fast Exact Planning in Markov Decision Processes, H. Brendan McMahan, Geoffrey J. Gordon, International Conference on Automated Planning and Scheduling (ICAPS), 2005.
Generalizing Dijkstra's algorithm and Gaussian Elimination for solving MDPs, H. Brendan McMahan, Geoffrey J. Gordon, CMU CSD TR number CMU-CS-05-127, 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 (SODA), 2005.
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.
Multi-source spanning trees: algorithms for minimizing source eccentricities, H. Brendan McMahan, A. Proskurowski, Discrete Applied Mathematics, vol. 137/2 (2003), pp. 213-222.
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.
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