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Results: 15
Number of items: 15
  • Open Access
    Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Allen, A., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J. A., Dong, H., Gupta, J. K., Thambiratnam, K., Archibald, A. T., Wu, C.-C., Heider, E., Welling, M., Turner, R. E., & Perdikaris, P. (2025). A foundation model for the Earth system. Nature, 641(8065), 1180-1187. https://doi.org/10.1038/s41586-025-09005-y
  • Open Access
    de Rijke, M., van den Hurk, B., Salim, F., Khourdajie, A. A., Bai, N., Calzone, R., Curran, D., Demil, G., Frew, L., Gießing, N., Gupta, M. K., Heuss, M., Hobeichi, S., Huard, D., Kang, J., Lucic, A., Mallick, T., Nath, S., Okem, A., ... Xie, Y. (2025). Report on the 1st Workshop on Information Retrieval for Climate Impact (MANILA24) at SIGIR 2024. SIGIR Forum, 59(1). https://doi.org/10.1145/3769733.3769737
  • Open Access
    Zhdanov, M., Ruhe, D., Weiler, M., Lucic, A., Forré, P. D., Brandstetter, J., & Forré, P. (2024). Clifford-steerable convolutional neural networks. Proceedings of Machine Learning Research, 235, 61203-612228. https://proceedings.mlr.press/v235/zhdanov24a.html
  • Open Access
    Lucic, A. (2022). Explaining predictions from machine learning models: algorithms, users, and pedagogy. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Neely, M., Schouten, S. F., Bleeker, M., & Lucic, A. (2022). A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing. 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. 60-78). (Frontiers in Artificial Intelligence and Applications; Vol. 354). IOS Press. https://doi.org/10.3233/FAIA220190
  • Open Access
    Lucic, A., Bleeker, M., Jullien, S., Bhargav, S., & de Rijke, M. (2022). Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence. 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. 11, pp. 12792-12800). AAAI Press. https://doi.org/10.1609/aaai.v36i11.21558
  • Open Access
    Lucic, A., Bleeker, M., Bhargav, S., Forde, J. Z., Sinha, K., Dodge, J., Luccioni, S., & Stojnic, R. (2022). ACL tutorial proposal: Towards Reproducible Machine Learning Research in Natural Language Processing. In L. Benotti, N. Okazaki, Y. Scherrer, & M. Zampieri (Eds.), The 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022 : tutorial abstracts : May 22-27, 2022 (pp. 7-11). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-tutorials.2
  • Open Access
    Lucic, A., ter Hoeve, M., Tolomei, G., de Rijke, M., & Silvestri, F. (2021). CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2102.03322
  • Open Access
    Lucic, A., ter Hoeve, M., Tolomei, G., de Rijke, M., & Silvestri, F. (2021). CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. In DLG-KDD’21: Deep Learning on Graphs, August 14–18, 2021, Online Article 3 ACM. https://doi.org/10.1145/1122445.1122456
  • Open Access
    Lucic, A., Srikumar, M., Bhatt, U., Xiang, A., Taly, A., Liao, Q. V., & de Rijke, M. (2021). A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms. Paper presented at HCXAI2021: ACM CHI Workshop Human-Centered Perspectives in Explainable AI, Yokohama, Japan. https://arxiv.org/abs/2103.14976
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