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H. Brendan McMahan
2010 – today
- 2013
[i8]H. Brendan McMahan: Minimax Optimal Algorithms for Unconstrained Linear Optimization. CoRR abs/1302.2176 (2013)- [i7]Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young: Large-Scale Learning with Less RAM via Randomization. CoRR abs/1303.4664 (2013)
- 2012
- [j6]H. Brendan McMahan, Matthew J. Streeter: Open Problem: Better Bounds for Online Logistic Regression. Journal of Machine Learning Research - Proceedings Track 23: 44.1-44.3 (2012)
- [c11]Matthew J. Streeter, H. Brendan McMahan: No-Regret Algorithms for Unconstrained Online Convex Optimization. NIPS 2012: 2411-2419
- [i6]Matthew J. Streeter, H. Brendan McMahan: No-Regret Algorithms for Unconstrained Online Convex Optimization. CoRR abs/1211.2260 (2012)
- [i5]H. Brendan McMahan, Omkar Muralidharan: On Calibrated Predictions for Auction Selection Mechanisms. CoRR abs/1211.3955 (2012)
- 2011
- [j5]H. Brendan McMahan: Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees". Journal of Machine Learning Research - Proceedings Track 15: 27-28 (2011)
- [j4]H. Brendan McMahan: Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization. Journal of Machine Learning Research - Proceedings Track 15: 525-533 (2011)
- 2010
- [c10]H. Brendan McMahan, Matthew J. Streeter: Adaptive Bound Optimization for Online Convex Optimization. COLT 2010: 244-256
- [i4]Matthew J. Streeter, H. Brendan McMahan: Less Regret via Online Conditioning. CoRR abs/1002.4862 (2010)
- [i3]H. Brendan McMahan, Matthew J. Streeter: Adaptive Bound Optimization for Online Convex Optimization. CoRR abs/1002.4908 (2010)
- [i2]H. Brendan McMahan: Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and Implicit Updates. CoRR abs/1009.3240 (2010)
2000 – 2009
- 2009
- [j3]Varun Kanade, H. Brendan McMahan, Brent Bryan: Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards. Journal of Machine Learning Research - Proceedings Track 5: 272-279 (2009)
- [c9]H. Brendan McMahan, Matthew J. Streeter: Tighter Bounds for Multi-Armed Bandits with Expert Advice. COLT 2009
- 2007
- [j2]H. Brendan McMahan, Geoffrey J. Gordon: A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games. Journal of Machine Learning Research - Proceedings Track 2: 323-330 (2007)
- [c8]H. Brendan McMahan, Geoffrey J. Gordon: A Unification of Extensive-Form Games and Markov Decision Processes. AAAI 2007: 86-93
- [c7]Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider: Efficiently computing minimax expected-size confidence regions. ICML 2007: 97-104
- [c6]Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta: Selecting Observations against Adversarial Objectives. NIPS 2007
- 2005
- [c5]H. Brendan McMahan, Geoffrey J. Gordon: Fast Exact Planning in Markov Decision Processes. ICAPS 2005: 151-160
- [c4]H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon: Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. ICML 2005: 569-576
- [c3]Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan: Online convex optimization in the bandit setting: gradient descent without a gradient. SODA 2005: 385-394
- 2004
- [j1]H. Brendan McMahan, Andrzej Proskurowski: Multi-source spanning trees: algorithms for minimizing source eccentricities. Discrete Applied Mathematics 137(2): 213-222 (2004)
- [c2]H. Brendan McMahan, Avrim Blum: Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. COLT 2004: 109-123
- [i1]Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan: Online convex optimization in the bandit setting: gradient descent without a gradient. CoRR cs.LG/0408007 (2004)
- 2003
- [c1]H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum: Planning in the Presence of Cost Functions Controlled by an Adversary. ICML 2003: 536-543
Coauthor Index
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last updated on 2013-04-09 21:28 CEST by the dblp team