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Results: 56
Number of items: 56
  • Open Access
    Gulshad, S. (2022). Explainable robustness for visual classification. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Kool, W., van Hoof, H., Gromicho, J., & Welling, M. (2022). Deep Policy Dynamic Programming for Vehicle Routing Problems. In P. Schaus (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022 : proceedings (pp. 190–213). (Lecture Notes in Computer Science; Vol. 13292). Springer. https://doi.org/10.48550/arXiv.2102.11756, https://doi.org/10.1007/978-3-031-08011-1_14
  • Open Access
    Wang, S., Zhang, C., Kröse, B., & van Hoof, H. (2021). Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. Journal of medical systems, 45(12), Article 102. https://doi.org/10.1007/s10916-021-01773-0
  • Open Access
    Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html
  • Open Access
    Bakker, T., Van Hoof, H., & Welling, M. (2021). Experimental design for MRI by greedy policy search. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 23, pp. 18954-18966). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html
  • Open Access
    Wang, S., Sporrel, K., van Hoof, H., Simons, M., de Boer, R. D. D., Ettema, D., Nibbeling, N., Deutekom, M., & Kröse, B. (2021). Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study. International Journal of Environmental Research and Public Health, 18(11), Article 6059. https://doi.org/10.3390/ijerph18116059
  • Open Access
    Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Zhang, Y., & van Hoof, H. (2021). Deep Coherent Exploration For Continuous Control. Proceedings of Machine Learning Research, 139, 12567-12577. https://proceedings.mlr.press/v139/zhang21t.html
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2021). Hierarchies of Planning and Reinforcement Learning for Robot Navigation. In 2021 IEEE International Conference on Robotics and Automation (ICRA 2021): May 31-June 4, 2021, Xi'an, China (pp. 10682-10688). IEEE. https://doi.org/10.48550/arXiv.2109.11178, https://doi.org/10.1109/ICRA48506.2021.9561151
  • Open Access
    van der Heiden, T., Mirus, F., & van Hoof, H. (2020). Social Navigation with Human Empowerment Driven Deep Reinforcement Learning. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020 : proceedings (Vol. II, pp. 395-407). (Lecture Notes in Computer Science; Vol. 12397). Springer. https://doi.org/10.1007/978-3-030-61616-8_32
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