Alan Lahoud

Artiklar, forskningsöversikter

• Lahoud, A. A. , Khan, A. S. , Schaffernicht, E. , Trincavelli, M. & Stork, J. A. (2025). Predict-and-Optimize Techniques for Data-Driven Optimization Problems: A Review.  Neural Processing Letters, 57 (2). [BibTeX]

Konferensbidrag

• Lahoud, A. , Schaffernicht, E. & Stork, J. A. (2025). Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems. Konferensbidrag vid 39th Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego Convention Center, San Diego, CA, United States, December 2-7, 2025. [BibTeX]
• Rickenbach, R. , Lahoud, A. , Schaffernicht, E. , Zeilinger, M. & Stork, J. A. (2025). ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning. Konferensbidrag vid 9th Conference on Robot Learning (CoRL 2025), Seoul, Korea, September 27-30, 2025. [BibTeX]
• Lahoud, A. , Schaffernicht, E. & Stork, J. A. (2024). DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context. I:  Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence. Konferensbidrag vid 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Barcelona, Spain, July 15-19, 2024. (ss. 2094-2112). JMLR. [BibTeX]
• Lahoud, A. A. , Schaffernicht, E. & Stork, J. A. (2024). Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks. I: Michael Wand; Kristína Malinovská; Jürgen Schmidhuber; Igor V. Tetko,  Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I. Konferensbidrag vid 33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN 2024), Lugano, Switzerland, September 17-20, 2024. (ss. 147-162). Springer. [BibTeX]