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Year: 2009
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Displaying results 21 to 30 of 58.

  1. Hannes Schulz; Kristian Kersting; Andreas Karwath

    ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries

    In: Luc De Raedt (Hrsg.). Inductive Logic Programming, 19th International Conference. International Conference on Inductive Logic Programming …

  2. Saket Joshi; Kristian Kersting; Roni Khardon

    Generalized First Order Decision Diagrams for First Order Markov Decision Processes

    In: Craig Boutilier (Hrsg.). IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. International Joint …

  3. Zhao Xu; Kristian Kersting; Volker Tresp

    Multi-Relational Learning with Gaussian Processes

    In: Craig Boutilier (Hrsg.). IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. International Joint …

  4. Novi Quadrianto; Kristian Kersting; Mark D. Reid; Tibério S. Caetano; Wray L. Buntine

    Kernel Conditional Quantile Estimation via Reduction Revisited

    In: Wei Wang; Hillol Kargupta; Sanjay Ranka; Philip S. Yu; Xindong Wu (Hrsg.). ICDM 2009, The Ninth IEEE International Conference on Data Mining. IEEE …

  5. Christian Thurau; Kristian Kersting; Christian Bauckhage

    Convex Non-negative Matrix Factorization in the Wild

    In: Wei Wang; Hillol Kargupta; Sanjay Ranka; Philip S. Yu; Xindong Wu (Hrsg.). ICDM 2009, The Ninth IEEE International Conference on Data Mining. IEEE …

  6. Matthew Hoffman; Nando de Freitas; Arnaud Doucet; Jan Peters

    An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

    In: David A. Van Dyk; Max Welling (Hrsg.). Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. …

  7. Jan Peters; Jun Morimoto; Russ Tedrake; Nicholas Roy

    Robot learning [TC Spotlight]

    In: IEEE Robotics & Automation Magazine, Vol. 16, No. 3, Pages 19-20, IEEE, 2009.

  8. Hirotaka Hachiya; Takayuki Akiyama; Masashi Sugiyama; Jan Peters

    Adaptive importance sampling for value function approximation in off-policy reinforcement learning

    In: Neural Networks, Vol. 22, No. 10, Pages 1399-1410, Elsevier, 2009.

  9. Marc Peter Deisenroth; Carl Edward Rasmussen; Jan Peters

    Gaussian process dynamic programming

    In: Neurocomputing, Vol. 72, No. 7-9, Pages 1508-1524, Elsevier, 2009.

  10. Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters

    Model Learning with Local Gaussian Process Regression

    In: Advanced Robotics, Vol. 23, No. 15, Pages 2015-2034, Taylor & Francis Online, 2009.

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