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
2026
Lumadjeng, A., Birbil, I., & Acar, E. (2026). ECSEL: Explainable Classification via Signomial Equation Learning. [No source information available]. http://adsabs.harvard.edu/abs/2026arXiv260121789L- Nguyen, L., Boersma, M., & Acar, E. (2026). Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations. In R. Guidotti, U. Schmid, & L. Longo (Eds.), Explainable Artificial Intelligence: Third World Conference, xAI 2025, Istanbul, Turkey, July 9–11, 2025 : proceedings (Vol. IV, pp. 330–353). (Communications in Computer and Information Science; Vol. 2579). Springer. https://doi.org/10.1007/978-3-032-08330-2_16 [details]
- van Breda, A., & Acar, E. (2026). Explaining the Explainer: Understanding the Inner Workings of Transformer-based Symbolic Regression Models. [No source information available]. https://doi.org/10.48550/arXiv.2602.03506
2025
- Chatterji, S., & Acar, E. (2025). Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning. In I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, A. Omicini, & M. Dastani (Eds.), ECAI 2025: 28th European Conference on Artificial Intelligence, 25-30 October2025, Bologna, Italy : including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025) : proceedings (pp. 2538-2545). (Frontiers in Artificial Intelligence and Applications; Vol. 413). IOS Press. https://doi.org/10.48550/arXiv.2411.04867, https://doi.org/10.3233/FAIA251103 [details]
- Kondylidis, N., Yaman, A., van Harmelen, F., Acar, E., & ten Teije, A. (in press). Successful Misunderstandings: Learning to Coordinate Without Being Understood. In Proceedings of EUMAS 2025 Springer. https://doi.org/10.48550/arXiv.2509.24660
- 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]
- Sauter, A., Salehkaleybar, S., Plaat, A., & Acar, E. (2025). ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder. [No source information available]. http://adsabs.harvard.edu/abs/2025arXiv250301290S
- Tasnim, M., Ghebreab, S., & Acar, E. (2025). EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets. [No source information available]. http://adsabs.harvard.edu/abs/2025arXiv250612033T
- Zhang, T., Williams, A., Wozny, P., Cohrs, K.-H., Ponse, K., Jiralerspong, M., Phade, S. R., Srinivasa, S., Li, L., Zhang, Y., Gupta, P., Acar, E., Rish, I., Bengio, Y., & Zheng, S. (2025). AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. Proceedings of Machine Learning Research, 267, 76332-76360. https://openreview.net/forum?id=PX29zF9wRb [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). 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 [details]
- 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]
- Sauter, A. W. M., Acar, E., & Plaat, A. (2024). CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research. [No source information available]. http://adsabs.harvard.edu/abs/2024arXiv240513092S
- 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 [details]
- 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 https://doi.org/10.48550/arXiv.2410.21928
2023
- Renting, B., Wozny, P., Loftin, R., Wieners, C., & Acar, E. (2023). AI4GCC - Team: Below Sea Level: Critiques and Improvements. [No source information available]. http://adsabs.harvard.edu/abs/2023arXiv230713894R
- 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]
- Wozny, P., Renting, B., Loftin, R., Wieners, C., & Acar, E. (2023). AI4GCC-Team -- Below Sea Level: Score and Real World Relevance. [No source information available]. http://adsabs.harvard.edu/abs/2023arXiv230713892W
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, S., & Arch-int, N. (2022). An argumentative approach for handling inconsistency in prioritized Datalog± ontologies. AI Communications, 35(3), 243-267. https://doi.org/10.3233/AIC-220087
- 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
2020
- Feng, R., Acar, E., Schlobach, S., Wang, Y., & Liu, W. (2020). On sufficient and necessary conditions in bounded CTL: A forgetting approach. In D. Calvanese, E. Erdem, & M. Thielscher (Eds.), 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020 (pp. 360-369). (17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020; Vol. 1). International Joint Conference on Artificial Intelligence (IJCAI).
2026
- Fonseca, J., Lô, G., Acar, E., & Santos, F. (2026). The Interplay between Media, Politics, and Online Behaviour: Measuring the Dynamics of Discriminatory Climates: Research for the State Commission against Discrimination and Racism. SIAS Research Group, Informatics Institute, Faculty of Science, University of Amsterdam. https://www.staatscommissietegendiscriminatieenracisme.nl/documenten/2026/02/11/voortgangsrapportage---tussen-kamer-krant-en-sociale-media [details]
2025
- van Sprang, A., Samson, L., Lucic, A., Acar, E., Ghebreab, S., & Asano, Y. M. (2025). Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs. ArXiv. https://doi.org/10.48550/arXiv.2512.08923
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
- Sauter, A. W. M., Acar, E., & Plaat, A. (2024). Causal Playground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2405.13092
- 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 [details]
2025
- van Sprang, A., Acar, E., & Zuidema, W. (2025). Interpretability for Time Series Transformers using A Concept Bottleneck Framework. Poster session presented at Mechanistic Interpretability Workshop, San Diego, California, United States. https://doi.org/10.48550/arXiv.2410.06070
2023
- Orzan, N., Acar, E., Grossi, D., & Rădulescu, R. (2023). Emergent Cooperation and Deception in Public Good Games. Paper presented at Adaptive and Learning Agents Workshop 2023, London, United Kingdom.
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Nevenwerkzaamheden
Geen nevenwerkzaamheden
Lumadjeng, A., Birbil, I., & Acar, E. (2026). ECSEL: Explainable Classification via Signomial Equation Learning. [No source information available].