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Results: 19
Number of items: 19
  • Open Access
    Birbil, İ., Martin, Ö., Onay, G., & Öztoprak, F. (2024). Bolstering stochastic gradient descent with model building. TOP, 32(3), 517-536. https://doi.org/10.1007/s11750-024-00673-z
  • 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
    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
  • 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
    Karaca, U., Birbil, S. I., Aydin, N., & Mullaoğlu, G. (2023). Masking Primal and Dual Models for Data Privacy in Network Revenue Management. European Journal of Operational Research, 308(2), 818-831. https://doi.org/10.1016/j.ejor.2022.11.025
  • Kuru, N., Birbil, S. I., Gurbuzbalaban, M., & Yildirim, S. (2022). Differentially Private Accelerated Optimization Algorithms. SIAM Journal on Optimization, 32(2), 795-821. https://doi.org/10.1137/20M1355847
  • 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
    Dekker, R., Koot, P., Birbil, S. I., & van Embden Andres, M. (2022). Co-designing Algorithms for Governance: Ensuring Responsible and Accountable Algorithmic Management of Refugee Camp Supplies. Big Data & Society, 9(1). https://doi.org/10.1177/20539517221087855
  • 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
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