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All publications by Drew Bagnell
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Learning Policies for Contextual Submodular Prediction
Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey and Drew Bagnell
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Efficient high dimensional maximum entropy modeling via symmetric partition functions
Paul Vernaza and Drew Bagnell
Advances in Neural Information Processing Systems 25, 2012


Agnostic System Identification for Model-Based Reinforcement Learning
Stephane Ross and Drew Bagnell
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


SpeedBoost: Anytime Prediction with Uniform Near-Optimality
Alexander Grubb and Drew Bagnell
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Computational Rationalization: The Inverse Equilibrium Problem
Kevin Waugh, Drew Bagnell and Brian D. Ziebart
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Generalized Boosting Algorithms for Convex Optimization
Alexander Grubb and Drew Bagnell
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
St├ęphane Ross, Geoffrey J. Gordon and Drew Bagnell
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Efficient Reductions for Imitation Learning
St├ęphane Ross and Drew Bagnell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


On Local Rewards and Scaling Distributed Reinforcement Learning
Drew Bagnell and Andrew Y. Ng
Advances in Neural Information Processing Systems 18, 2005