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 |
Hermans, J. (2022). Advances in Simulation-Based Inference: Towards the automation of the Scientific Method through Learning Algorithms [Doctoral thesis, ULiège - University of Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/289425 |
Hermans, J., Banik, N., Weniger, C., Bertone, G., & Louppe, G. (2021). Towards constraining warm dark matter with stellar streams through neural simulation-based inference. Monthly Notices of the Royal Astronomical Society. doi:10.1093/mnras/stab2181 Peer Reviewed verified by ORBi |
Hermans, J., Banik, N., Weniger, C., Bertone, G., & Louppe, G. (11 December 2020). Probing Dark Matter Substructure with Stellar Streams and Neural Simulation-Based Inference [Poster presentation]. Machine Learning and the Physical Sciences. Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS). Peer reviewed |
Hermans, J., Begy, V., & Louppe, G. (2020). Likelihood-free MCMC with Amortized Approximate Ratio Estimators. In Proceedings of the 37th International Conference on Machine Learning (pp. 4239-4248). Peer reviewed |
Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (19 November 2019). Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. Astrophysical Journal, 886 (1). doi:10.3847/1538-4357/ab4c41 Peer Reviewed verified by ORBi |
Louppe, G., Hermans, J., & Cranmer, K. (08 November 2019). Adversarial Variational Optimization of Non-Differentiable Simulators [Poster presentation]. AI Synergies, Brussels, Belgium. |
Louppe, G., Hermans, J., & Cranmer, K. (2019). Adversarial Variational Optimization of Non-Differentiable Simulators. Proceedings of Machine Learning Research. Peer Reviewed verified by ORBi |
Volodimir, B., Hermans, J., Barisits, M., Lassnig, M., & Schikuta, E. (2019). Simulating Data Access Profiles of Computational Jobs in Data Grids. IEEE International Conference on eScience. doi:10.1109/eScience.2019.00051 Peer reviewed |
Hermans, J., & Louppe, G. (2018). Gradient Energy Matching for Distributed Asynchronous Gradient Descent. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/226232. |