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Results: 8
Number of items: 8
  • Open Access
    Lucic, A. (2022). Explaining predictions from machine learning models: algorithms, users, and pedagogy. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Lippe, P., Ren, P., Haned, H., Voorn, B., & de Rijke, M. (2022). Simultaneously Improving Utility and User Experience in Task-oriented Dialogue Systems. Paper presented at The 2022 SIGIR Workshop On eCommerce, Madrid, Spain. https://sigir-ecom.github.io/ecom22Papers/paper_5042.pdf
  • Open Access
    Wilms, M., Sileno, G., & Haned, H. (2022). PEBAM: A Profile-Based Evaluation Method for Bias Assessment on Mixed Datasets. In R. Bergmann, L. Malburg, S. C. Rodermund, & I. J. Timm (Eds.), KI 2022: Advances in Artificial Intelligence: 45th German Conference on AI, Trier, Germany, September 19–23, 2022 : proceedings (pp. 209-223). (Lecture Notes in Computer Science; Vol. 13404), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-031-15791-2_17
  • 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
  • Open Access
    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
  • Open Access
    Lucic, A., Oosterhuis, H., Haned, H., & de Rijke, M. (2019). Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. (v1 ed.) ArXiv.
  • Open Access
    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
  • Open Access
    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
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