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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]
Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J. W. (2023). Topological Obstructions and How to Avoid Them. In Conference on Neural Information Processing Systems (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation.
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]
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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]
Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J. W. (2023). Topological Obstructions and How to Avoid Them. Paper presented at 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, United States. https://openreview.net/attachment?id=1tviRBNxI9&name=pdf
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Zimmermann, H. (2025). Variational inference for probabilistic programs and generative models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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