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All publications by Michael L. Littman
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Apprenticeship Learning About Multiple Intentions
Monica Babes, Vukosi Marivate, Kaushik Subramanian and Michael L. Littman
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
Michael Wunder, Michael L. Littman and Monica Babes
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Generalizing Apprenticeship Learning across Hypothesis Classes
Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman and Carlos Diuk
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Provably Efficient Learning with Typed Parametric Models
Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman and Nicholas Roy
Journal of Machine Learning Research, 2009


Reinforcement Learning in Finite MDPs: PAC Analysis
Alexander L. Strehl, Lihong Li and Michael L. Littman
Journal of Machine Learning Research, 2009


Democratic approximation of lexicographic preference models
Fusun Yaman, Thomas J. Walsh, Michael L. Littman and Marie Desjardins
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-wakefield and Michael L. Littman
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Knows what it knows: a framework for self-aware learning
Lihong Li, Michael L. Littman and Thomas J. Walsh
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


An object-oriented representation for efficient reinforcement learning
Carlos Diuk, Andre Cohen and Michael L. Littman
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Multi-resolution Exploration in Continuous Spaces
Ali Nouri and Michael L. Littman
Advances in Neural Information Processing Systems 21, 2008


Analyzing feature generation for value-function approximation
Ronald Parr, Christopher Painter-wakefield, Lihong Li and Michael L. Littman
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Online Linear Regression and Its Application to Model-Based Reinforcement Learning
Alexander L. Strehl and Michael L. Littman
Advances in Neural Information Processing Systems 20, 2007


Experience-efficient learning in associative bandit problems
Alexander L. Strehl, Chris Mesterharm, Michael L. Littman and Haym Hirsh
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


PAC model-free reinforcement learning
Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford and Michael L. Littman
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


A theoretical analysis of Model-Based Interval Estimation
Alexander L. Strehl and Michael L. Littman
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Learning Predictive State Representations
Satinder P. Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe and Peter Stone
Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003


PAC Generalization Bounds for Co-training
Sanjoy Dasgupta, Michael L. Littman and David A. Mcallester
Advances in Neural Information Processing Systems 14, 2001


An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games
Michael L. Littman, Michael J. Kearns and Satinder P. Singh
Advances in Neural Information Processing Systems 14, 2001