Dr. rer. nat. E. (Erman) Acar
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 900
Postadres
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Postbus 94323
1090 GH Amsterdam
Contactgegevens
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Publicaties
2025
Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2025). Learning in public goods games: the effects of uncertainty and communication on cooperation. Neural Computing and Applications, 37(23), 18899–18932. https://doi.org/10.1007/s00521-024-10530-6 [details]
2024
- 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 [details]
- Gerdes, W., & Acar, E. (2024). Integrating Fuzzy Logic into Deep Symbolic Regression. In Workshop for Explainable AI in Finance 2024, New York
- Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2024). Learning in Public Goods Games with Non-Linear Utilities: a Multi-Objective Approach. In Proc. of the Adaptive and Learning Agents Workshop (ALA 2024)
- Orzan, N., Acar, E., Grossi, D., Mannion, P., & Rădulescu, R. (2024). Learning in Multi-Objective Public Goods Games with Non-Linear Utilities. In U. Endriss, F. S. Melo, K. Bach, A. Bugarín-Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Eds.), ECAI 2024: 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain : including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) : proceedings (pp. 2749-2756). (Frontiers in Artificial Intelligence and Applications; Vol. 392). IOS Press. https://doi.org/10.3233/FAIA240809 [details]
- Orzan, N., Acar, E., Radulescu, R., & Grossi, D. (2024). Emergent Cooperation under Uncertain Incentive Alignment. In N. Alechina, V. Dignum, M. Dastani, & J. S. Sichman (Eds.), 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.
- Sauter, A. W. M., Acar, E., & Plaat, A. (2024). Causal Playground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research. In ArXiv
- Sauter, A. W. M., Boteghi, N., Acar, E., & Plaat, A. (2024). CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning. In N. Alechina, V. Dignum, M. Dastani, & J. S. Sichman (Eds.), 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.
- Visbeek, S., Acar, E., & den Hengst, F. (2024). Explainable Fraud Detection with Deep Symbolic Classification. 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. 350–373). (Communications in Computer and Information Science; Vol. 2155). Springer. https://doi.org/10.1007/978-3-031-63800-8_18 [details]
- Wolfson, B., & Acar, E. (2024). Differentiable Inductive Logic Programming for Fraud Detection. In Workshop for Explainable AI in Finance 2024, New York
2023
- Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2023). Emergent Cooperation and Deception in Public Good Games. In Proc. of the Adaptive and Learning Agents Workshop (ALA 2023)
- 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 [details]
2022
- 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 [details]
- 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 [details]
- Ho, L., Acar, E., Arch-int, , S., Schlobach, K. S., & Arch-int, N. (2022). An argumentative approach for handling inconsistency in prioritized Datalog± ontologies. AI Communications.
- Verma, M., & Acar, E. (2022). Learning to Cooperate with Human Evaluative Feedback and Demonstrations. In S. Schlobach, M. Pérez-Ortiz, & M. Tielman (Eds.), HHAI2022: Augmenting Human Intellect: Proceedings of the 1st International Conference on Hybrid Human-Artificial Intelligence (pp. 46-59). (Frontiers in Artificial Intelligence and Applications; Vol. 354). IOS Press. https://doi.org/10.3233/FAIA220189
- van Krieken, E., Acar, E., & van Harmelen, F. (2022). Analyzing Differentiable Fuzzy Logic Operators. Artificial Intelligence, 302, Article 103602. https://doi.org/10.1016/j.artint.2021.103602
2024
- Azarm, C., Acar, E., & van Zeelt, M. (2024). On the Potential of Network-Based Features for Fraud Detection. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2402.09495
- van Sprang, A. V., Zuidema, W. H., & Acar, E. (2024). Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework. Manuscript submitted for publication. In Enforcing Interpretability in Time Series Transformers: A Concept Bottleneck Framework
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Nevenwerkzaamheden
Geen nevenwerkzaamheden
Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2025). Learning in public goods games: the effects of uncertainty and communication on cooperation. Neural Computing and Applications, 37(23), 18899–18932.