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Results: 17
Number of items: 17
  • Open Access
    van Sprang, A., Acar, E., & Zuidema, W. (2024). Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2410.06070
  • Open Access
    Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2024). Emergent Cooperation under Uncertain Incentive Alignment. In AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems : May 6-10, 2024, Auckland, New Zealand (pp. 1521-1530). International Foundation for Autonomous Agents and Multiagent Systems. https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1521.pdf
  • Open Access
    Sauter, A., Boteghi, N., Acar, E., & Plaat, A. (2024). CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning. In AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems : May 6-10, 2024, Auckland, New Zealand (pp. 1664-1672). International Foundation for Autonomous Agents and Multiagent Systems. https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1664.pdf
  • Open Access
    Breuer, N. O., Sauter, A., Mohammadi, M., & Acar, E. (2024). CAGE: Causality-Aware Shapley Value for Global Explanations. In L. Longo, S. Lapuschkin, & C. Seifert (Eds.), Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024 : proceedings (Vol. III, pp. 143–162). (Communications in Computer and Information Science; Vol. 2155). Springer. https://doi.org/10.1007/978-3-031-63800-8_8
  • Open Access
    Sauter, A., Acar, E., & François-Lavet, V. (2023). A Meta-Reinforcement Learning Algorithm for Causal Discovery. Proceedings of Machine Learning Research, 213, 602-619. https://doi.org/10.48550/arXiv.2207.08457
  • Feng, R., Acar, E., Wang, Y., Schlobach, S., Liu, W., & Ding, W. (2022). Computing Sufficient and Necessary Conditions in CTL: A Forgetting Approach. Information Sciences, 616, 474-504. https://doi.org/10.1016/j.ins.2022.10.124
  • Open Access
    GhadimiAtigh, M., Schoep, J., Acar, E., van Noord, N., & Mettes, P. (2022). Hyperbolic Image Segmentation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: New Orleans, Louisiana, 19-24 June 2022 : proceedings (pp. 4443-4452). (CVPR). IEEE Computer Society. https://doi.org/10.1109/CVPR52688.2022.00441
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