Search results
Results: 24
Number of items: 24
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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 -
von Stackelberg, P., Goedhart, R., Birbil, S. I., & Does, R. J. M. M. (2024). Comparison of threshold tuning methods for predictive monitoring. Quality and Reliability Engineering International, 40(1), 499-512. https://doi.org/10.1002/qre.3436
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Kuiper, A., & Goedhart, R. (2024). Optimized Control Charts using Indifference Regions. Quality Engineering, 36(2), 371-389. https://doi.org/10.1080/08982112.2023.2218904
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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
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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
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Wang, Z., Goedhart, R., & Zwetsloot, I. M. (2023). Monitoring high-dimensional heteroscedastic processes using rank-based EWMA methods. Computers & Industrial Engineering, 184, Article 109544. https://doi.org/10.1016/j.cie.2023.109544
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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
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Huberts, L. C. E., Goedhart, R., & Does, R. J. M. M. (2022). Improved control chart performance using cautious parameter learning. Computers & Industrial Engineering, 169, Article 108185. https://doi.org/10.1016/j.cie.2022.108185
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Goedhart, R., & Woodall, W. H. (2022). Monitoring proportions with two components of common cause variation. Journal of Quality Technology, 54(3), 324-337. https://doi.org/10.1080/00224065.2021.1903823
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