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Results: 56
Number of items: 56
  • Open Access
    Caccia, L., van Hoof, H., Courville, A., & Pineau, J. (2019). Deep Generative Modeling of LiDAR Data. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Macau, China, 3-8 November 2019 (pp. 5034-5040). IEEE. https://doi.org/10.1109/IROS40897.2019.8968535
  • Barbaros, V., van Hoof, H., Abdolmaleki, A., & Meger, D. (2018). Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control. In 2018 IEEE International Conference on Robotics and Automation (ICRA): May 21-25, 2018, Brisbane, Australia (pp. 5090-5096). IEEE. https://doi.org/10.1109/ICRA.2018.8460633
  • Open Access
    Smith, M. J. A., Van Hoof, H., & Pineau, J. (2018). An Inference-Based Policy Gradient Method for Learning Options. Proceedings of Machine Learning Research, 80, 4703-4712. http://proceedings.mlr.press/v80/smith18a.html
  • Open Access
    Manjanna, S., van Hoof, H., & Dudek, G. (2018). Reinforcement Learning with Non-uniform State Representations for Adaptive Search. In 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics: SSRR 2018 : Philadelphia, PA, USA on August 6-8, 2018 IEEE. https://doi.org/10.1109/SSRR.2018.8468649
  • Open Access
    Fujimoto, S., van Hoof, H., & Meger, D. (2018). Addressing function approximation error in actor-critic methods. Proceedings of Machine Learning Research, 80, 1587-1596. http://proceedings.mlr.press/v80/fujimoto18a.html
  • Open Access
    Dong, Y., Shen, Y., Crawford, E., van Hoof, H., & Cheung, J. C. K. (2018). BanditSum: Extractive Summarization as a Contextual Bandit. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing : EMNLP 2018: Brussels, Belgium, Oct. 31-Nov. 4 (pp. 3739-3748). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1409
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