Search results
Results: 7
Number of items: 7
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Kurtz, J., Birbil, Ş. İ., & den Hertog, D. (2026). Counterfactual explanations for linear optimization. European Journal of Operational Research, 329(1), 24-41. https://doi.org/10.1016/j.ejor.2025.06.016 -
Goerigk, M., & Kurtz, J. (2025). Data-driven prediction of relevant scenarios for robust combinatorial optimization. Computers & Operations Research, 174, Article 106886. https://doi.org/10.1016/j.cor.2024.106886
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Dumouchelle, J., Julien, E., Kurtz, J., & Khalil, E. B. (2025). Neur2BiLO: Neural Bilevel Optimization. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 86688-86719). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-2752
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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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Kurtz, J. (2024). Approximation Guarantees for Min-Max-Min Robust Optimization and k-Adaptability Under Objective Uncertainty. SIAM Journal on Optimization, 34(2), 2121-2149. https://doi.org/10.1137/23M1595084
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Goerigk, M., & Kurtz, J. (2023). Data-Driven Robust Optimization using Unsupervised Deep Learning. Computers & Operations Research, 151, Article 106087. https://doi.org/10.1016/j.cor.2022.106087
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Kurtz, J. (2022). Ensemble Methods for Robust Support Vector Machines using Integer Programming. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2203.01606
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