  | Marcos Alvarez, A., Louveaux, Q., & Wehenkel, L. (January 2017). A Machine Learning-Based Approximation of Strong Branching. INFORMS Journal on Computing, 29 (1), 185-195. doi:10.1287/ijoc.2016.0723  Peer Reviewed verified by ORBi |
 | Marcos Alvarez, A. (2016). Computational and Theoretical Synergies between Linear Optimization and Supervised Machine Learning [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/204460 |
 | Marcos Alvarez, A., Wehenkel, L., & Louveaux, Q. (2016). Online Learning for Strong Branching Approximation in Branch-and-Bound. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/192361. |
 | Marcos Alvarez, A., Wehenkel, L., & Louveaux, Q. (2015). Machine Learning to Balance the Load in Parallel Branch-and-Bound. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/181086. |
 | Marcos Alvarez, A. (2014). Economic statistical design of nonparametric control charts [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/178825 |
 | Pierard, S., Marcos Alvarez, A., Lejeune, A., & Van Droogenbroeck, M. (2014). On-the-fly domain adaptation of binary classifiers. In 23rd Belgian-Dutch Conference on Machine Learning (BENELEARN) (pp. 20-28). Peer reviewed |
 | Marcos Alvarez, A., Louveaux, Q., & Wehenkel, L. (2014). A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/167559. |
 | Marcos Alvarez, A., Yamada, M., & Kimura, A. (2013). Exploiting Socially-Generated Side Information in Dimensionality Reduction. In Proceedings of the 2nd International Workshop on Socially-Aware Multimedia. New York, United States: ACM. doi:10.1145/2509916.2509923 Peer reviewed |
 | Kimura, A., Ishiguro, K., Marcos Alvarez, A., Kataoka, K., Murasaki, K., & Yamada, M. (2013). Image Context Discovery from Socially Curated Contents. In Proceedings of the 21st ACM International Conference on Multimedia. New York, United States: ACM. doi:10.1145/2502081.2502149 Peer reviewed |
 | Marcos Alvarez, A., Yamada, M., Kimura, A., & Iwata, T. (2013). Clustering-Based Anomaly Detection in Multi-View Data. In Proceedings of the 22nd International Conference on Information and Knowledge Management. New York, United States: ACM. doi:10.1145/2505515.2507840 Peer reviewed |
 | Marcos Alvarez, A., Maes, F., & Wehenkel, L. (2012). Supervised learning to tune simulated annealing for in silico protein structure prediction. In M. Verleysen (Ed.), ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 49-54). Louvain-la-Neuve, Belgium: Ciaco. Peer reviewed |
 | Marcos Alvarez, A. (21 February 2012). Supervised learning to tune simulated annealing for in silico protein structure prediction [Poster presentation]. Bridging statistical physics and optimization, inference and learning, Les Houches, France. |
 | Marcos Alvarez, A. (2011). Prédiction de structures de macromolécules par apprentissage automatique [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/103916 |