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Results: 56
Number of items: 56
  • Open Access
    Höpner, N. R. (2026). Algorithms for knowledge-guided sequential decision-making: Integrating graphs, demonstrations, human and cross-agent experience. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hoepner, N., Tiddi, I., & van Hoof, H. (2025). Data Augmentation for Instruction Following Policies via Trajectory Segmentation. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence: February 25-March 4, 2025, Philadelphia, Pennsylvania, USA (Vol. 16, pp. 17214-17222). AAAI Press. https://doi.org/10.1609/aaai.v39i16.33892
  • Open Access
    Hoepner, N., Kuric, D., & van Hoof, H. (2025). Making Universal Policies Universal. In Y. Vorobeychik, S. Das, & A. Nowe (Eds.), AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems : May 19-23, 2025, Detroit, Michigan, USA (pp. 2553-2555). International Foundation for Autonomous Agents and Multiagent Systems. https://doi.org/10.48550/arXiv.2502.14777
  • Open Access
    Mussi, M., Metelli, A. M., Restelli, M., Losapio, G., Bessa, R. J., Boos, D., Borst, C., Leto, G., Castagna, A., Chavarriaga, R., Dias, D., Egli, A., Eisenegger, A., El Manyari, Y., Fuxjäger, A., Geraldes, J., Hamouche, S., Hassouna, M., Lemetayer, B., ... Zanotti, G. (2025). Human-AI interaction in safety-critical network infrastructures. iScience, 28(9), Article 113400. https://doi.org/10.1016/j.isci.2025.113400
  • Open Access
    Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024). Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. In SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 14-18, 2024, Washington, DC, USA (pp. 416-426). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657749
  • Open Access
    Loftin, R., Çelikok, M. M., van Hoof, H., Kaski, S., & Oliehoek, F. A. (2024). Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. In AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems : May 6-10, 2024, Auckland, New Zealand (pp. 1265-1273). International Foundation for Autonomous Agents and Multiagent Systems. https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1265.pdf
  • Open Access
    Kuric, D., Infante, G., Gómez, V., Jonsson, A., & van Hoof, H. (2024). Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. In S. Bernardini, & C. Muise (Eds.), Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling: June 1–6, 2024, Alberta, Canada (pp. 333-341). (ICAPS; Vol. 34). AAAI Press. https://doi.org/10.1609/icaps.v34i1.31492
  • Open Access
    Wöhlke, J. G. (2024). Reinforcement learning and planning for autonomous agent navigation: With a focus on sparse reward settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Giri, C., Granmo, O. C., & Van Hoof, H. (2024). Accelerated Tsetlin Machine Inference Through Incremental Model Re-evaluation. In 2024 International Symposium on the Tsetlin Machine (ISTM 2024): Pittsburgh, Pennsylvania, USA, 28-30 August 2024 (pp. 93-100). IEEE. https://doi.org/10.1109/ISTM62799.2024.10931270
  • Open Access
    Mansoury, M., Mobasher, B., & van Hoof, H. (2024). Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits. In CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management : October, 21-25. 2024, Boise, ID, USA (pp. 1638-1648). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679763
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