  | Duchesne, L., Louveaux, Q., & Wehenkel, L. (2021). Supervised learning of convex piecewise linear approximations of optimization problems. In Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. doi:10.14428/esann/2021.ES2021-74  Peer reviewed |
 | Duchesne, L. (2021). Machine Learning of Proxies for Power Systems Reliability Management in Operation Planning [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/260398 |
 | 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 |
 | Duchesne, L., Karangelos, E., & Wehenkel, L. (2020). Recent Developments in Machine Learning for Energy Systems Reliability Management. Proceedings of the IEEE. doi:10.1109/JPROC.2020.2988715 Peer Reviewed verified by ORBi |
 | Duchesne, L., Savelli, I., & Cornélusse, B. (2019). Sensitivity Analysis of a Local Market Model for Community Microgrids. In PowerTech Milano 2019 Proceedings. doi:10.1109/PTC.2019.8810614 Peer reviewed |
 | Duchesne, L., Karangelos, E., & Wehenkel, L. (2018). Using Machine Learning to Enable Probabilistic Reliability Assessment in Operation Planning. In Power Systems Computation Conference 2018 Proceedings. PSCC. doi:10.23919/PSCC.2018.8442566 Peer reviewed |
 | Duchesne, L., Karangelos, E., & Wehenkel, L. (2017). Machine Learning of Real-time Power Systems Reliability Management Response. In PowerTech Manchester 2017 Proceedings. doi:10.1109/PTC.2017.7980927 Peer reviewed |