Dijkman, J., Dijkstra, M., van Roij, R., Welling, M., van de Meent, J.-W., & Ensing, B. (2025). Learning Neural Free-Energy Functionals with Pair-Correlation Matching. Physical Review Letters, 134(5), Article 056103. https://doi.org/10.1103/PhysRevLett.134.056103[details]
Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html[details]
Ram, K., Dijkman, J., van Roij, R., Van de Meent, J.-W., Ensing, B., Welling, M., & Cremers, D. (2025). Learned free-energy functionals from pair-correlation matching for dynamical density functional theory. Physical Review E, 112(4), Article 045314. https://doi.org/10.1103/22fd-ykkb
2024
Eijkelboom, F., Bartosh, G., Naesseth, C. A., Welling, M., & van de Meent, J. W. (2024). Variational Flow Matching for Graph Generation. In Advances in Neural Information Processing Systems (Vol. 37). (Advances in Neural Information Processing Systems). Neural Information Processing Systems Foundation.
McInerney, D. J., Dickinson, W., Flynn, L. C., Young, A. C., Young, G. S., van de Meent, J.-W., & Wallace, B. C. (2024). Towards Reducing Diagnostic Errors with Interpretable Risk Prediction. In K. Duh, H. Gomez, & S. Bethard (Eds.), The 2024 Conference of the North American Chapter of the Association for Computational Linguistics : proceedings of the conference: NAACL 2024 : June 16-21, 2024 (Vol. 1, pp. 7193-7210). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.399[details]
Zimmermann, H., Naesseth, C. A., & van de Meent, J. W. (2024). VISA: Variational Inference with Sequential Sample-Average Approximations. In Advances in Neural Information Processing Systems (Vol. 37). (Advances in Neural Information Processing Systems). Neural Information Processing Systems Foundation.
2023
Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J.-W. (2023). Topological Obstructions and How to Avoid Them. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/1c12ccfc7720f6b680edea17300bfc2b-Abstract-Conference.html[details]
Zimmermann, H., Lindsten, F., van de Meent, J.-W., & Naesseth, C. A. (2023). A Variational Perspective on Generative Flow Networks. Transactions on Machine Learning Research, 2023, Article 612. https://openreview.net/forum?id=AZ4GobeSLq[details]
Esmaeili, B., Wu, H., Zimmermann, H., & Van De Meent, J.-W. (2022). Nested Variational Inference. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 25, pp. 20423-20435). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper_files/paper/2021/hash/ab49b208848abe14418090d95df0d590-Abstract.html[details]
Smedemark-Margulies, N., Walters, R., Zimmermann, H., Laird, L., van der Loo, C., Kaushik, N., Caceres, R., & van de Meent, J. W. (2022). Probabilistic program inference in network-based epidemiological simulations. PLoS Computational Biology, 18(11), Article e1010591. https://doi.org/10.1371/journal.pcbi.1010591[details]
Zaghen, O., Eijkelboom, F., Pouplin, A., Liu, C., Welling, M., van de Meent, J.-W., & Bekkers, E. J. (2025). Riemannian Variational Flow Matching for Material and Protein Design. ArXiv. https://doi.org/10.48550/arXiv.2502.12981
2025
Zimmermann, H. (2025). Variational inference for probabilistic programs and generative models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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