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All publications by J. A. Bagnell
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Modeling Interaction via the Principle of Maximum Causal Entropy
Brian D. Ziebart, J. A. Bagnell and Anind K. Dey
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Boosted Backpropagation Learning for Training Deep Modular Networks
Alexander Grubb and J. A. Bagnell
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Inverse Optimal Heuristic Control for Imitation Learning
Nathan D. Ratliff, Brian D. Ziebart, Kevin Peterson, J. A. Bagnell, Martial Hebert, Anind K. Dey and Siddhartha S. Srinivasa
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Differentiable Sparse Coding
J. A. Bagnell and David M. Bradley
Advances in Neural Information Processing Systems 21, 2008


(Approximate) Subgradient Methods for Structured Prediction
Nathan D. Ratliff, J. A. Bagnell and Martin Zinkevich
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Maximum margin planning
Nathan D. Ratliff, J. A. Bagnell and Martin Zinkevich
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Boosting Structured Prediction for Imitation Learning
J. A. Bagnell, Joel Chestnutt, David M. Bradley and Nathan D. Ratliff
Advances in Neural Information Processing Systems 19, 2006


Policy Search by Dynamic Programming
J. A. Bagnell, Sham M. Kakade, Jeff G. Schneider and Andrew Y. Ng
Advances in Neural Information Processing Systems 16, 2003