Alan Lahoud
Alan Lahoud Position: Doctoral Student School/office: School of Science and TechnologyEmail: YWxhbi5sYWhvdWQ7b3J1LnNl
Phone: +46 19 301226
Room: T1209
Research subject
Research environments
About Alan Lahoud
Alan is a Ph.D. student in the field of Computer Science and is affiliated to the WASP research program. Alan's project has a collaboration with H&M Group as part of the CoAIRob school. Alan is a member of the Adaptive and Interpretable Learning Systems (AILS) lab. His research interests include Probabilistic Machine Learning and their applications to Optimization Problems.
Research groups
Publications
Articles, reviews/surveys |
Conference papers |
Articles, reviews/surveys
- 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]
Conference papers
- Lahoud, A. , Schaffernicht, E. & Stork, J. A. (2025). Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems. Paper presented at 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. Paper presented at 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. In: Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence. Paper presented at 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Barcelona, Spain, July 15-19, 2024. (pp. 2094-2112). JMLR. [BibTeX]
- Lahoud, A. A. , Schaffernicht, E. & Stork, J. A. (2024). Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks. In: 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. Paper presented at 33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN 2024), Lugano, Switzerland, September 17-20, 2024. (pp. 147-162). Springer. [BibTeX]