A. (Ana) Lucic PhD
Faculty of Science
ILLC
Visiting address
- Science Park 900
Postal address
-
Postbus 94242
1090 GE Amsterdam
Contact details
-
Publications
- refereed (6)
- academic (3)
- conference contributions (2)
- doctoral thesis (1)
- working papers / preprints (1)
2022
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 [details]- 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. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12792-12800. https://doi.org/10.1609/aaai.v36i11.21558 [details]
- Lucic, A., Bleeker, M., de Rijke, M., Sinha, K., Jullien, S., & Stojnic, R. (2022). Towards Reproducible Machine Learning Research in Information Retrieval. In SIGIR '22: proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 11-15, 2022, Madrid, Spain The Association for Computing Machinery.
2021
- 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 [details]
2020
- Lucic, A., Haned, H., & de Rijke, M. (2020). Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting. In FAT* '20: proceedings of the 2020 Conference on Fairness, Accountability, and Transparency : January 27-30, 2020, Barcelona, Spain (pp. 90-98). The Association for Computing Machinery. https://doi.org/10.1145/3351095.3372824 [details]
2019
- Olteanu, A., Garcia-Gathright, J., de Rijke, M., Ekstrand, M. D., Roegiest, A., Lipani, A., Beutel, A., Lucic, A., Stoica, A.-A., Das, A., Biega, A., Voorn, B., Hauff, C., Spina, D., Lewis, D., Oard, D. W., Yilmaz, E., Hasibi, F., Kazai, G., ... Kamishima, T. (2019). FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval. SIGIR Forum, 53(2), 20-43. http://sigir.org/wp-content/uploads/2019/december/p020.pdf [details]
2021
- 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 [details]
2019
- Lucic, A., Haned, H., & de Rijke, M. (2019). Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations. In T. Miller, R. Weber, & D. Magazzeni (Eds.), Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence (pp. 66-72). IJCAI. https://arxiv.org/abs/1908.00085v1 [details]
- Lucic, A., Haned, H., & de Rijke, M. (2019). Explaining Predictions from Tree-based Boosting Ensembles. In Proceedings of FACTS-IR 2019 ArXiv. https://arxiv.org/abs/1907.02582 [details]
2022
- Lucic, A., Oosterhuis, H., Haned, H., & de Rijke, M. (2022). FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. Poster session presented at 36th AAAI Conference on Artificial Intelligence (AAAI-2022). https://doi.org/10.48550/arXiv.1911.12199
2021
- 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 [details]
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Ancillary activities
No ancillary activities
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.