  | Falkiewicz, M., Takeishi, N., Shekhzadeh, I., Wehenkel, A., Delaunoy, A., Louppe, G., & Kalousis, A. (2023). Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. Advances in Neural Information Processing Systems. doi:10.48550/arXiv.2310.13402  Peer Reviewed verified by ORBi |
 | Théate, T., Wehenkel, A., Bolland, A., Louppe, G., & Ernst, D. (14 May 2023). Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks. Neurocomputing, 534, 199-219. doi:10.1016/j.neucom.2023.02.049 Peer Reviewed verified by ORBi |
 | Wehenkel, A., Behrmann, J., Hsu, H., Sapiro, G., Louppe, G., & Jacobsen, J.-H. (2023). Robust Hybrid Learning With Expert Augmentation. Transactions on Machine Learning Research. Peer Reviewed verified by ORBi |
 | Delaunoy, A.* , Hermans, J.* , Rozet, F., Wehenkel, A., & Louppe, G. (2022). Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., & Louppe, G. (2022). A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful. Transactions on Machine Learning Research. Peer Reviewed verified by ORBi |
 | Wehenkel, A. (2022). Inductive Bias In Deep Probabilistic Modelling [Doctoral thesis, ULiège - University of Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/294546 |
 | 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 |
 | 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 |
 | Wehenkel, A., & Louppe, G. (July 2021). Diffusion Priors In Variational Autoencoders [Poster presentation]. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models. Peer reviewed |
 | Wehenkel, A., & Louppe, G. (2021). Graphical Normalizing Flows. In Proceedings of AISTATS 2021. Peer reviewed |
 | Vandegar, M., Kagan, M., Wehenkel, A., & Louppe, G. (2021). Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. In Proceedings of AISTATS 2021. Peer reviewed |
 | Delaunoy, A., Wehenkel, A., Hinderer, T., Nissanke, S., Weniger, C., Williamson, A., & Louppe, G. (11 December 2020). Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS). Peer reviewed |
 | Wehenkel, A., & Louppe, G. (10 July 2020). You say Normalizing Flows I see Bayesian Networks [Poster presentation]. ICML2020 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models. Peer reviewed |
 | Vecoven, N., Ernst, D., Wehenkel, A., & Drion, G. (2020). Introducing neuromodulation in deep neural networks to learn adaptive behaviours. PLoS ONE. doi:10.1371/journal.pone.0227922 Peer Reviewed verified by ORBi |
 | Wehenkel, A., Mukhopadhyay, A., Le Boudec, J.-Y., & Paolone, M. (2020). Parameter Estimation of Three-Phase Untransposed Short Transmission Lines from Synchrophasor Measurements. IEEE Transactions on Instrumentation and Measurement. doi:10.1109/TIM.2020.2969059 Peer Reviewed verified by ORBi |
 | Vecoven, N., Ernst, D., Wehenkel, A., & Drion, G. (2019). Cellular neuromodulation in artificial networks. In Proceedings of the NeurIPS 2019 Workshop Neuro AI. Peer reviewed |
 | Wehenkel, A., & Louppe, G. (2019). Unconstrained Monotonic Neural Networks. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi |
 | Pesah, A., Wehenkel, A., & Louppe, G. (08 December 2018). Recurrent machines for likelihood-free inference [Poster presentation]. Workshop of Meta-Learning at Thirty-second Conference on Neural Information Processing Systems 2018, Montreal, Canada. Peer reviewed |
 | Dubois, A., Wehenkel, A., Fonteneau, R., Olivier, F., & Ernst, D. (2017). An App-based Algorithmic Approach for Harvesting Local and Renewable Energy Using Electric Vehicles. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017). doi:10.5220/0006250803220327 Peer reviewed |
