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Boelrijk, J., van Herwerden, D., Ensing, B., Forré, P., & Samanipour, S. (2023). Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. Journal of Cheminformatics, 15(1), 28. https://doi.org/10.1186/s13321-023-00699-8
Bos, T. S., Boelrijk, J., Molenaar, S. R. A., Veer, B. V. ., Niezen, L. E., van Herwerden, D., Samanipour, S., Stoll, D. R., Forré, P., Ensing, B., Somsen, G. W., & Pirok, B. W. J. (2022). Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. Analytical Chemistry, 94(46), 16060-16068. https://doi.org/10.1021/acs.analchem.2c03160[details]
Cole, A., Forre, P., Louppe, G., Miller, B. K., & Weniger, C. (2022). Truncated Marginal Neural Ratio Estimation. 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. 1, pp. 129-143). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/01632f7b7a127233fa1188bd6c2e42e1-Abstract.html[details]
Forre, P., Hoogeboom, E., Jaini, P., Nielsen, D., & Welling, M. (2022). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. 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. 15, pp. 12454-12465). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/67d96d458abdef21792e6d8e590244e7-Abstract.html[details]
Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. https://doi.org/10.1111/2041-210X.14007[details]
Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. D. (2022). Physics-informed inference of aerial animal movements from weather radar data. In NeurIPS 2022 AI for Science workshop Neural Information Processing Systems Foundation. https://arxiv.org/abs/2211.04539
Maile, K. M. L., Wilson, D., & Forré, P. D. (2022). Towards architectural optimization of equivariant neural networks over subgroups. In NeurIPS 2022 Workshop: NeurReps - Symmetry and Geometry in Neural Representations Neural Information Processing Systems Foundation. https://openreview.net/forum?id=KJFpArxWe-g
Ruhe, D. J. J., Wong, K., Cranmer, M., & Forré, P. D. (2022). Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. In NeurIPS 2022 Workshop: Machine Learning and the Physical Sciences Neural Information Processing Systems Foundation. https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_126.pdf
Ruhe, D., Kuiack, M., Rowlinson, A., Wijers, R., & Forré, P. (2022). Detecting dispersed radio transients in real time using convolutional neural networks. Astronomy and Computing, 38, [100512]. https://doi.org/10.1016/j.ascom.2021.100512[details]
Apostol, A. C., Stol, M. C., & Forré, P. (2021). FlipOut: Uncovering Redundant Weights via Sign Flipping. In M. Baratchi, L. Cao, W. A. Kosters, J. Lijffijt, J. N. van Rijn, & F. W. Takes (Eds.), Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020 : revised selected papers (pp. 15-29). (Communications in Computer and Information Science; Vol. 1398). Springer. https://doi.org/10.1007/978-3-030-76640-5_2[details]
Bongers, S., Forré, P., Peters, J., & Mooij, J. M. (2021). Foundations of structural causal models with cycles and latent variables. The Annals of Statistics, 49(5), 2885-2915. https://doi.org/10.1214/21-AOS2064[details]
Falorsi, L., de Haan, P., Davidson, T. R., & Forré, P. (2019). Reparameterizing Distributions on Lie Groups. Proceedings of Machine Learning Research, 89, 3244-3253. https://arxiv.org/abs/1903.02958[details]
Forré, P., & Mooij, J. M. (2019). Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 [15] AUAI Press. http://auai.org/uai2019/proceedings/papers/15.pdf[details]
Patrini, G., van den Berg, R., Forré, P., Carioni, M., Bhargav, S., Welling, M., Genewein, T., & Nielsen, F. (2019). Sinkhorn AutoEncoders. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 [253] AUAI Press. https://arxiv.org/abs/1810.01118[details]
Forré, P., & Mooij, J. M. (2018). Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 269-278). AUAI Press. http://auai.org/uai2018/proceedings/papers/117.pdf[details]
Boelrijk, J. H. M., Ensing, B., & Forré, P. D. (2022). Multi-objective optimization via equivariant deep hypervolume approximation. https://arxiv.org/abs/2210.02177
Lang, L., Baudot, P., Quax, R., & Forré, P. D. (2022). Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. https://arxiv.org/abs/2202.09393
Maile, K. M. L., Wilson, D., & Forré, P. D. (2022). Architectural Optimization over Subgroups for Equivariant Neural Networks. https://doi.org/10.48550/arXiv.2210.05484
Pandeva, T. P., & Forré, P. D. (2022). Multi-View Independent Component Analysis with Shared and Individual Sources. https://arxiv.org/abs/2210.02083
Pandeva, T. P., Bakker, T. B., Andersson Naesseth, C. A., & Forré, P. D. (2022). E-Valuating Classifier Two-Sample Tests. https://arxiv.org/abs/2210.13027
Keller, T. A., Peters, J. W. T., Jaini, P., Hoogeboom, E., Forré, P., & Welling, M. (2021). Self Normalizing Flows. Proceedings of Machine Learning Research, 139, 5378-5387. https://arxiv.org/abs/2011.07248[details]
Miller, B. K., Weniger, C., & Forré, P. D. (2022). Contrastive Neural Ratio Estimation. Paper presented at Thirty-sixth Conference on Neural Information Processing Systems, New Orleans, Louisiana, United States. https://openreview.net/forum?id=kOIaB1hzaLe
Ruhe, D. J. J., & Forré, P. D. (2022). Self-Supervised Inference in State-Space Models. Paper presented at The Tenth International Conference on Learning Representations. https://openreview.net/forum?id=VPjw9KPWRSK
2021
Federici, M., Tomioka, R., & Forré, P. D. (2021). An Information-theoretic Approach to Distribution Shifts. Paper presented at NeurIPS 2021. https://arxiv.org/pdf/2106.03783.pdf
Gallego-Posada, J., & Forré, P. D. (2021). Simplicial Regularization. Paper presented at ICLR 2021 Workshop: Geometrical and Topological Representation Learning. https://openreview.net/forum?id=x9xn6HKgefz
2020
Falorsi, L., & Forré, P. D. (2020). Neural Ordinary Differential Equations on Manifolds. Paper presented at ICML 2020 workshop INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, . https://arxiv.org/abs/2006.06663
Federici, M., Dutta, A., Forré, P., Kushman, N., & Akata, Z. (2020). Learning Robust Representations via Multi-View Information Bottleneck. Paper presented at 8th International Conference on Learning Representations, Addis Abeba, Ethiopia.
2018
Falorsi, L., de Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., & Cohen, T. S. (2018). Explorations in Homeomorphic Variational Auto-Encoding. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden. [details]
Ilse, M., Tomczak, J. M., & Forré, P. (2020). Selecting Data Augmentation for Simulating Interventions. (v4 ed.) arXiv.org. https://arxiv.org/abs/2005.01856[details]
Forré, P., & Mooij, J. M. (2017). Markov Properties for Graphical Models with Cycles and Latent Variables. Amsterdam: Informatics Institute, University of Amsterdam. [details]
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