  | Schumacher, P., & Sutera, A. (2022). Analyse comparative de post-édition et de traduction humaine en contexte académique. In C. Expósito Castro, M. D. M. Ogea Pozo, ... F. Rodríguez Rodríguez, Theory and practice of translation as a vehicle for knowledge transfer (pp. 173-208). Séville, Spain: Editorial Universidad de Sevilla.  Peer reviewed |
 | Dumas, J., Wehenkel, A., Lanaspeze, D., Cornélusse, B., & Sutera, A. (01 January 2022). A deep generative model for probabilistic energy forecasting in power systems: normalizing flows. Applied Energy, 305, 117-871. doi:10.1016/j.apenergy.2021.117871 Peer Reviewed verified by ORBi |
 | Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2021). From global to local MDI variable importances for random forests and when they are Shapley values. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi |
 | Marulli, D., Mathieu, S., Benzerga, A., Sutera, A., & Ernst, D. (2021). Reconstruction of low-voltage networks with limited observability. In IEEE PES Innovative Smart Grid Technologies Conference Europe. doi:10.1109/ISGTEurope52324.2021.9640163 Peer reviewed |
 | Dumas, J., Cointe, C., Wehenkel, A., Sutera, A., Fettweis, X., & Cornélusse, B. (2021). A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market. IEEE Transactions on Sustainable Energy. doi:10.1109/TSTE.2021.3117594 Peer Reviewed verified by ORBi |
 | Benzerga, A., Maruli, D., Sutera, A., Bahmanyar, A., Mathieu, S., & Ernst, D. (2021). Low-voltage network topology and impedance identification using smart meter measurements. In Proceedings of the 2021 IEEE Madrid PowerTech. doi:10.1109/PowerTech46648.2021.9495093 Peer reviewed |
  | Vecoven, N., Begon, J.-M., Sutera, A., Geurts, P., & Huynh-Thu, V. A. (2020). Nets versus trees for feature ranking and gene network inference. In Proceeding of the 23rd International Conference on Discovery Science (DS 2020). Springer. doi:10.1007/978-3-030-61527-7_16 Peer reviewed |
 | Duchesne, L., Karangelos, E., Sutera, A., & Wehenkel, L. (2020). Machine Learning for Ranking Day-ahead Decisions in the Context of Short-term Operation Planning. Electric Power Systems Research. doi:10.1016/j.epsr.2020.106548 Peer Reviewed verified by ORBi |
 | Sutera, A. (2019). Importance measures derived from random forests: characterisation and extension [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/236868 |
 | Sutera, A. (2019). Lecture on "Variable selection using random forests" (R. Genuer et al., 2010). (ULiège - Université de Liège, INFO8004 - Advanced Machine Learning). |
 | Wehenkel, M., Sutera, A., Bastin, C., Geurts, P.* , & Phillips, C.*. (29 June 2018). Random Forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Frontiers in Neuroscience, 12, 411. doi:10.3389/fnins.2018.00411 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Olivier, F., Sutera, A., Geurts, P., Fonteneau, R., & Ernst, D. (2018). Phase Identification of Smart Meters by Clustering Voltage Measurements. In Proceedings of the XX Power Systems Computation Conference (PSCC 2018). doi:10.23919/PSCC.2018.8442853 Peer reviewed |
 | Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (2018). Random Subspace with Trees for Feature Selection Under Memory Constraints. In A. Storkey & F. Perez-Cruz (Eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (pp. 929-937). Playa Blanca, Spain: PMLR. Peer reviewed |
 | Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Ernst, D., & Geurts, P. (2017). Simple connectome inference from partial correlation statistics in calcium imaging. In J. Soriano, D. Battaglia, I. Guyon, V. Lemaire, J. Orlandi, ... B. Ray (Eds.), Neural Connectomics Challenge (pp. 23-36). Springer. doi:10.1007/978-3-319-53070-3 Peer reviewed |
 | Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (12 September 2016). Random subspace with trees for feature selection under memory constraints [Poster presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium. Peer reviewed |
 | Sutera, A. (24 August 2016). Random forests variable importances Towards a better understanding and large-scale feature selection [Paper presentation]. 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain. |
 | Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2016). Context-dependent feature analysis with random forests. In Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016). Peer reviewed |
 | Taralla, D., Qiu, Z., Sutera, A., Fonteneau, R., & Ernst, D. (2016). Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning: A Study Intended for Large-Scale Video Games. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 2 (pp. 264-271). doi:10.5220/0005666202640271 Peer reviewed |
 | Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2014). Simple connectome inference from partial correlation statistics in calcium imaging. In J. Soriano, D. Battaglia, I. Guyon, V. Lemaire, J. Orlandi, ... B. Ray (Eds.), Neural Connectomics Challenge. Springer. Peer reviewed |
 | Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Advances in Neural Information Processing Systems 26. Peer reviewed |
 | Sutera, A. (2013). Characterization of variable importance measures derived from decision trees [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/157155 |