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Results: 89
Number of items: 89
  • Hofmann, K., Whiteson, S., & de Rijke, M. (2011). Balancing exploration and exploitation in learning to rank online. In P. Clough, C. Foley, C. Gurrin, G. J. F. Jones, W. Kraaij, H. Lee, & V. Murdoch (Eds.), Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011 : proceedings (pp. 251-263). (Lecture Notes in Computer Science; Vol. 6611). Springer. https://doi.org/10.1007/978-3-642-20161-5_25
  • Hofmann, K., Whiteson, S., & de Rijke, M. (2011). Adapting Rankers Online. In A. Hanbury, A. Rauber, & A. P. de Vries (Eds.), Multidisciplinary Information Retrieval: Second Information Retrieval Facility Conference, IRFC 2011, Vienna, Austria, June 6, 2011: proceedings (pp. 1-2). (Lecture Notes in Computer Science; Vol. 6653). Springer. https://doi.org/10.1007/978-3-642-21353-3_1
  • Hofmann, K., Whiteson, S., & de Rijke, M. (2011). A Probabilistic Method for Inferring Preferences from Clicks. In CIKM'11: proceedings of the 2011 ACM International Conference on Information and Knowledge Management : October 24-28, 2011, Glasgow, Scotland (pp. 249-258). Association for Computing Machinery. https://doi.org/10.1145/2063576.2063618
  • Snel, M., Whiteson, S., & Kuniyoshi, Y. (2011). Robust central pattern generators for embodied hierarchical reinforcement learning. In 2011 IEEE International Conference on Development and Learning (ICDL) (pp. 1-6). IEEE. https://doi.org/10.1109/DEVLRN.2011.6037352
  • Hofmann, K., Whiteson, S., & de Rijke, M. (2011). Contextual Bandits for Information Retrieval. In NIPS 2011: Proceedings of the Conference on Neural Information Processing Systems, Workshop on Bayesian Optimization, Experimental Design and Bandits: Theory and Applications (pp. 1-5). NIPS. http://staff.science.uva.nl/~whiteson/pubs/hofmannnips11.pdf
  • Kistemaker, S., & Whiteson, S. (2011). Critical factors in the performance of novelty search. In N. Krasnogor (Ed.), GECCO 2011: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 965-972). ACM. https://doi.org/10.1145/2001576.2001708
  • Whiteson, S., Tanner, B., Taylor, M. E., & Stone, P. (2011). Protecting against evaluation overfitting in empirical reinforcement learning. In Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011) (pp. 120-127). IEEE. https://doi.org/10.1109/ADPRL.2011.5967363
  • Open Access
    van Seijen, H., Whiteson, S., van Hasselt, H., & Wiering, M. (2011). Exploiting best-match equations for efficient reinforcement learning. Journal of Machine Learning Research, 12, 2045-2094. http://jmlr.csail.mit.edu/papers/v12/vanseijen11a.html
  • Open Access
    Koppejan, R., & Whiteson, S. (2011). Neuroevolutionary reinforcement learning for generalized control of simulated helicopters. Evolutionary Intelligence, 4(4), 219-241. https://doi.org/10.1007/s12065-011-0066-z
  • Open Access
    van Seijen, H. H. (2011). Reinforcement learning under space and time constraints. [Thesis, fully internal, Universiteit van Amsterdam].
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