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Results: 15
Number of items: 15
  • 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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