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Results: 56
Number of items: 56
  • Open Access
    Van Hoof, H., & Wang, Q. (2023). Learning Expressive Meta-Representations with Mixture of Expert Neural Processes. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 34, pp. 26242-26255). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/a815fe7cad6af20a6c118f2072a881d2-Abstract-Conference.html
  • Open Access
    Hoogeboom, E. (2023). Normalizing flows and diffusion models for discrete and geometric data. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Kuric, D., & van Hoof, H. (2023). Reusable Options through Gradient-based Meta Learning. Transactions on Machine Learning Research, 2023(3), Article 717. https://openreview.net/forum?id=qdDmxzGuzu
  • Open Access
    Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2022). Value Refinement Network (VRN). In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: IJCAI 2022, Vienna, Austria, 23-29 July 2022 (pp. 3558-3565). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/494
  • Open Access
    Gulshad, S. (2022). Explainable robustness for visual classification. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Giri, C., Granmo, O.-C., van Hoof, H., & Blakely, C. D. (2022). Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine. In 2022 International Joint Conference on Neural Networks (IJCNN): 2022 conference proceedings (pp. 5528-5536). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892796
  • Open Access
    Long, A., Blair, A., & van Hoof, H. (2022). Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation. In K. Sycara, V. Honavar, & M. Spaan (Eds.), Proceedings of the 36th AAAI Conference on Artificial Intelligence: AAAI-22 : virtual conference, Vancouver, Canada, February 22-March 1, 2022 (Vol. 7, pp. 7620-7627). AAAI Press. https://doi.org/10.1609/aaai.v36i7.20728
  • Open Access
    Höpner, N., Tiddi, I., & van Hoof, H. (2022). Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: IJCAI 2022, Vienna, Austria, 23-29 July 2022 (pp. 3050-3056). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/423
  • Open Access
    Wang, Q., & van Hoof, H. (2022). Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. Proceedings of Machine Learning Research, 162, 23055-23077. https://proceedings.mlr.press/v162/wang22z.html
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