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Profil

Hermans Joeri

Main Referenced Co-authors
Louppe, Gilles  (9)
Cranmer, Kyle (3)
Banik, Nilanjan (2)
Bertone, Gianfranco (2)
Delaunoy, Arnaud  (2)
Main Referenced Keywords
Statistics - Machine Learning (4); Computer Science - Learning (3); and Cluster Computing (1); approximate inference (1); Astrophysics - Cosmology and Nongalactic Astrophysics (1);
Main Referenced Disciplines
Computer science (11)
Space science, astronomy & astrophysics (3)
Physics (1)

Publications (total 11)

The most downloaded
855 downloads
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 https://hdl.handle.net/2268/289425

The most cited

54 citations (Scopus®)

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 https://hdl.handle.net/2268/239562

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.

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