Search results
Results: 56
Number of items: 56
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Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252 -
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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Shang, W., van der Wal, D., van Hoof, H., & Welling, M. (2020). Stochastic Activation Actor Critic Methods. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019 : proceedings (Vol. III, pp. 103-117). (Lecture Notes in Computer Science; Vol. 11908), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-46133-1_7
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Wang, Q., & van Hoof, H. (2020). Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. Proceedings of Machine Learning Research, 119, 10018-10028. http://proceedings.mlr.press/v119/wang20s.html
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Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1503-1511). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398934
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Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587
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Manjanna, S., Van Hoof, H., & Dudek, G. (2020). Policy Search on Aggregated State Space for Active Sampling. In J. Xiao, T. Kröger, & O. Khatib (Eds.), Proceedings of the 2018 International Symposium on Experimental Robotics (pp. 211-221). (Springer Proceedings in Advanced Robotics; Vol. 11). Springer. https://doi.org/10.1007/978-3-030-33950-0_19
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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
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Kool, W., van Hoof, H., & Welling, M. (2019). Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. Proceedings of Machine Learning Research, 97, 3499-3508. http://proceedings.mlr.press/v97/kool19a.html
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Thakur, S., van Hoof, H., Gamboa Higuera, J. C., Precup, D., & Meger, D. (2019). Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. In 2019 International Conference on Robotics and Automation (ICRA) : Montreal, Quebec, Canada, 20-24 May 2019 (Vol. 1, pp. 768-774). IEEE. https://doi.org/10.1109/ICRA.2019.8794328
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