ICML papers

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  1. Control of Memory, Active Perception, and Action in Minecraft.
    by Junhyuk Oh, Valliappa Chockalingum, Satinder Singh, and Honglak Lee.
    In 33rd International Conference on Machine Learning (ICML), 2016.
    pdf.

  2. Abstraction Selection in Model-Based Reinforcement Learning.
    by Nan Jiang, Alex Kulesza, and Satinder Singh.
    In 32nd International Conference on Machine Learning (ICML), 2015.
    pdf.

  3. Internal Rewards Mitigate Agent Boundedness
    by Jonathan Sorg, Satinder Singh, and Richard Lewis.
    In Proceedings of the 27th International Conference on Machine Learning (ICML), 2010.
    pdf

  4. Efficiently Learning Linear-Linear Exponential Family Predictive Representations of State
    by David Wingate and Satinder Singh.
    In Proceedings of the 25th International Conference on Machine Learning (ICML), pages 1176-1183, 2008.
    pdf

  5. Kernel Predictive Linear-Gaussian Models for Nonlinear Stochastic Dynamical Systems
    by David Wingate and Satinder Singh.
    In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 1017-1024, 2006.
    pdf

  6. Predictive linear-Gaussian models of controlled stochastic dynamical systems
    by Matthew Rudary and Satinder Singh.
    In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 777-784, 2006.
    pdf

  7. Predictive State Representations with Options
    by Britton Wolfe and Satinder Singh.
    In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 1025-1032, 2006.
    pdf

  8. Learning Predictive State Representations in Dynamical Systems Without Reset
    by Britton Wolfe, Michael R. James and Satinder Singh.
    In Proceedings of the 22nd International Conference on Machine Learning (ICML), 2005.
    pdf

  9. Learning and Discovery of Predictive State Representations in Dynamical Systems with Reset by Michael James and Satinder Singh. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), pages 417-424, 2004.
    pdf.

  10. Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-Based Temporal Reasoning by Matthew Rudary, Satinder Singh and Martha Pollack. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML), pages 719-726, 2004.
    pdf.

  11. Learning Predictive State Representations by Satinder Singh, Michael Littman, Nicholas Jong, David Pardoe and Peter Stone. In Proceedings of the Twentieth International Conference on Machine Learning (ICML), pages 712-719, 2003.
    gzipped postscript.

  12. A Boosting Approach to Topic Spotting on Subdialogues by Kary Myers, Michael Kearns, Satinder Singh and Marilyn Walker. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML) pages 655-662, 2000.
    gzipped postscript pdf.

  13. Eligibility Traces for Off-Policy Policy Evaluation by Doina Precup, Richard Sutton, and Satinder Singh. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 759-766, 2000.
    gzipped postscript pdf.

  14. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes by John Loch and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 323-331, 1998.
    gzipped postscript.

  15. Near-Optimal Reinforcement Learning in Polynomial Time by Michael Kearns and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 260-268, 1998.
    gzipped postscript.

  16. Intra-Option Learning about Temporally Abstract Actions by Richard Sutton, Doina Precup and Satinder Singh. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML), pages 556-564, 1998.
    gzipped postscript.

  17. Learning Without State-Estimation in Partially Observable Markovian Decision Processes by Satinder Singh, Tommi Jaakkola and Michael Jordan. In Machine Learning: Proceedings of the Eleventh International Conference (ICML), pages 284-292, 1994.
    gzipped postscript pdf.

  18. Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models by Satinder Singh. In Proceedings of the Ninth Machine Learning Conference, (ICML) pages 406-415, 1992.
    gzipped postscript.

  19. Transfer of Learning Across Compositions of Sequential Tasks by Satinder Singh. In Machine Learning: Proceedings of the Eighth International Workshop, (ICML) pages 348-352, 1991.
    gzipped postscript.
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