Communications publiées lors de congrès ou colloques nationaux et internationaux (49)
  1. 3. Aler Tubella, A., Coelho Mollo, D., Dahlgren Lindström, A., Devinney, H., Dignum, V., Ericson, P., Jonsson, A., Kampik, T., Lenaerts, T., Mendez, J. A., & Nieves, J. C. (2023). ACROCPoLis: A Descriptive Framework for Making Sense of Fairness. In Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 (pp. 1014-1025) Association for Computing Machinery. doi:10.1145/3593013.3594059
  2. 4. Nachtegael, C., De Stefani, J., & Lenaerts, T. (2023). ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (17 ed., pp. 117--127) Association for Computational Linguistics.
  3. 5. Abels, A., Lenaerts, T., Trianni, V., & Nowe, A. (2023). Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. In Proceedings of the 40th International Conference on Machine Learning: ICML'23: Vol. 202 (pp. 79-90). (Proceedings of Machine Learning Research). PMLR.
  4. 6. Abels, A., Lenaerts, T., Trianni, V., & Nowe, A. (2020). How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems. Computational Collective Intelligence: LNAI 12496 (pp. 125-138) International Conference on Computational Collective Intelligence(12: 2020: Da Nang, Vietnam). doi:10.1007/978-3-030-63007-2_10
  5. 7. Abels, A., Lenaerts, T., Trianni, V., & Nowe, A. (2020). Collective Decision-Making as a Contextual Multi-armed Bandit Problem. Computational Collective Intelligence: LNAI 12496 (pp. 113-124) International Conference on Computational Collective Intelligence(12: 2020: Da Nang, Vietnam). doi:10.1007/978-3-030-63007-2_9
  6. 8. Coppens, Y., Efthymiadis, K., Lenaerts, T., & Nowé, A. (2019). Distilling Deep Reinforcement Learning Policies in Soft Decision Trees. Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence (Macau, China)
  7. 9. Abels, A., Roijers, D. D., Lenaerts, T., Nowe, A., & Steckelmacher, D. (2019). Dynamic Weights in Multi-Objective Deep Reinforcement Learning. In Proceedings of the 36th International Conference on Machine Learning: Vol. 97 (pp. 11-20). (Proceedings of Machine Learning Research). PMLR.
  8. 10. Starzec, G., Starzec, M., Byrski, A., Kisiel-Dorohinicki, M., Burguillo, J. C., & Lenaerts, T. (2019). Towards Large-Scale Optimization of Iterated Prisoner Dilemma Strategies. In Transactions on Computational Collective Intelligence XXXII. (Lecture Notes in Computer Science,, 11370). Heidelberg, Berlin: Springer.
  9. 11. Han, T. A. T., Pereira, L. M., & Lenaerts, T. (2019). Modelling and influencing the AI bidding War: A research agenda. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. (AIES'19 series). Association for Computing Machinery. doi:10.1145/3306618.3314265
  10. 12. Abels, A., Roijers, D. D., & Lenaerts, T. (2018). Dynamic Weights in Multi-Objective Deep Reinforcement Learning. In Proceedings of the 30th Benelux Conference on Artificial Intelligence (pp. 1-2). (CCIS series). Springer.
  11. 13. Han, T. A. T., Pereira, L. M., Martinez-Vaquero, L. A., & Lenaerts, T. (2017). Centralized vs. Personalized Commitments and their influence on Cooperation in Group Interactions. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17) 31st AAAI Conference on Artificial Intelligence (AAAI-17)(4-9 February 2017: San Francisco, USA)
  12. 14. Fernandez Domingos, E., Burguillo-Rial, J. C., & Lenaerts, T. (2017). Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17) 31st AAAI Conference on Artificial Intelligence (AAAI-17)(4-9 February 2017: San Francisco, USA)
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