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Results: 17
Number of items: 17
  • Open Access
    Vogels, L., Mohammadi, R., Schoonhoven, M., & Birbil, S. I. (2024). Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison. Journal of the American Statistical Association, 119(548), 3164-3182. https://doi.org/10.1080/01621459.2024.2395504
  • 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
  • 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
  • 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
    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
    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
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