Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 8
Number of items: 8
  • Open Access
    Röber, T. E. (2026). Interpretable machine learning: Optimization-based explanations and human-centered evaluation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Röber, T. E., Lumadjeng, A. C., Akyuz, M. H., & Birbil, S. İ. B. (2025). Rule generation for classification: Scalability, interpretability, and fairness. Computers & Operations Research, 183, Article 107163. https://doi.org/10.1016/j.cor.2025.107163
  • Röber, T. E. (2025, October 6). Processed data counterfactual online study [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.30285109.v2
  • Open Access
    Röber, T. E., Goedhart, R., & Birbil, Ş. İ. (2025). Clinicians’ Voice: Fundamental Considerations for XAI in Healthcare. Proceedings of Machine Learning Research, 298. https://proceedings.mlr.press/v298/rober25a.html
  • Open Access
    Maragno, D., Kurtz, J., Röber, T. E., Goedhart, R., Birbil, Ş. İ., & den Hertog, D. (2024). Finding regions of counterfactual explanations via robust optimization. INFORMS Journal on Computing, 36(5), 1316–1334. https://doi.org/10.1287/ijoc.2023.0153
  • Open Access
    Cina, G., Röber, T. E., Goedhart, R., & Birbil, S. I. (2023). Semantic match: Debugging feature attribution methods in XAI for healthcare. Proceedings of Machine Learning Research, 209, 182-191. https://proceedings.mlr.press/v209/cina23a.html
  • Open Access
    Cina, G., Röber, T., Goedhart, R., & Birbil, I. (2022). Why we do need Explainable AI for Healthcare. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2206.15363
  • Open Access
    Maragno, D., Röber, T. E., & Birbil, S. İ. (2022). Counterfactual Explanations Using Optimization With Constraint Learning. In OPT2022: Optimization for Machine Learning. Accepted papers OPT-ML. https://doi.org/10.48550/arXiv.2209.10997
Page of