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Results: 21
Number of items: 21
  • Open Access
    Schubert, M., Claassen, T., & Magliacane, S. (2025). SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph. Proceedings of Machine Learning Research, 258, 3340-3348. https://proceedings.mlr.press/v258/schubert25a.html
  • Open Access
    Pîslar, T.-M., Magliacane, S., & Geiger, A. (2025). Combining Causal Models for More Accurate Abstractions of Neural Networks. Proceedings of Machine Learning Research, 275, 114-138. https://proceedings.mlr.press/v275/pislar25a.html
  • Open Access
    van Geloven, N., Keogh, R. H., van Amsterdam, W., Cinà, G., Krijthe, J. H., Peek, N., Luijken, K., Magliacane, S., Morzywołek, P., van Ommen, T., Putter, H., Sperrin, M., Wang, J., Weir, D. L., & Didelez, V. (2025). The Risks of Risk Assessment: Causal Blind Spots When Using Prediction Models for Treatment Decisions. Annals of Internal Medicine, 178(9), 1326-1333. https://doi.org/10.7326/ANNALS-24-00279
  • Open Access
    Lippe, P. (2025). Learning causal representations in spatio-temporal systems. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Meimetis, N., Pullen, K. M., Zhu, D. Y., Nilsson, A., Hoang, T. N., Magliacane, S., & Lauffenburger, D. A. (2024). AutoTransOP: translating omics signatures without orthologue requirements using deep learning. Npj Systems Biology and Applications, 10, Article 13. https://doi.org/10.1038/s41540-024-00341-9
  • Open Access
    Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., & Magliacane, S. (2024). A Sparsity Principle for Partially Observable Causal Representation Learning. Proceedings of Machine Learning Research, 235, 55389-55433. https://proceedings.mlr.press/v235/xu24ac.html
  • Open Access
    Liu, Y., Magliacane, S., Kofinas, M., & Gavves, E. (2024). Amortized Equation Discovery in Hybrid Dynamical Systems. Proceedings of Machine Learning Research, 235, 31645-31668. https://proceedings.mlr.press/v235/liu24at.html
  • Open Access
    Luijken, K., Morzywołek, P., van Amsterdam, W., Cinà, G., Hoogland, J., Keogh, R., Krijthe, J. H., Magliacane, S., van Ommen, T., Peek, N., Putter, H., van Smeden, M., Sperrin, M., Wang, J., Weir, D. L., Didelez, V., & van Geloven, N. (2024). Risk‐Based Decision Making: Estimands for Sequential Prediction Under Interventions. Biometrical Journal, 66(8), Article e70011. https://doi.org/10.1002/bimj.70011
  • Open Access
    Feng, F., & Magliacane, S. (2023). Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning. 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 (pp. 19117-19144). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-0838
  • Open Access
    Liu, Y., Magliacane, S., Kofinas, M., & Gavves, E. (2023). Graph switching dynamical systems. Proceedings of Machine Learning Research, 202, 21867-21883. https://proceedings.mlr.press/v202/liu23z.html
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