Dr. P.D. (Patrick) Forré
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 900
- Kamernummer: L4.03
Postadres
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Postbus 19268
1000 GG Amsterdam
Contactgegevens
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Publicaties
- refereed (24)
- wetenschappelijk (12)
- congresbijdragen (9)
- erkenning (2)
- supervised theses (3)
- working papers / preprints (7)
- datasets (3)
2024
Boelrijk, J., Molenaar, S. R. A., Bos, T. S., Dahlseid, T. A., Ensing, B., Stoll, D. R., Forré, P., & Pirok, B. W. J. (2024). Enhancing LC×LC separations through multi-task Bayesian optimization. Journal of Chromatography A, 1726, Article 464941. https://doi.org/10.1016/j.chroma.2024.464941 [details]- Federici, M., Forré, P. D., Tomioka, R., & Veeling, B. S. (in press). Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. In ICLR 2024: 12th International Conference on Learning Representations OpenReview. https://doi.org/10.48550/arXiv.2309.07200
- Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2024). Towards detailed and interpretable hybrid modeling of continental-scale bird migration. In AI for Science Workshop at the 41st International Conference on Machine Learning (ICML), 2024 https://doi.org/10.48550/arXiv.2407.10259
- Liu, C., Ruhe, D., Eijkelboom, F., & Forré, P. (in press). Clifford Group Equivariant Simplicial Message Passing Neural Networks. In The International Conference on Learning Representations 2024 OpenReview. https://doi.org/10.48550/arXiv.2402.10011
2023
- Boelrijk, J., Ensing, B., Forré, P., & Pirok, B. W. J. (2023). Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization. Analytica Chimica Acta, 1242, Article 340789. https://doi.org/10.1016/j.aca.2023.340789 [details]
- 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), Article 28. https://doi.org/10.1186/s13321-023-00699-8 [details]
- Forré, P., Miller, B. K., & Weniger, C. (2023). Contrastive Neural Ratio Estimation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 5, pp. 3262-3278). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2210.06170 [details]
- Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. D. (in press). Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.08445
- Ruhe, D. J. J., Forré, P. D., & Brandstetter, J. K. (2023). Clifford Group Equivariant Neural Networks. Advances in Neural Information Processing Systems.
2022
- Apostol, A. C., Stol, M. C., & Forré, P. (2022). Pruning by leveraging training dynamics. AI Communications, 35(2), 65-85. Advance online publication. https://doi.org/10.3233/AIC-210127 [details]
- 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. Advance online publication. https://doi.org/10.1111/2041-210X.14007 [details]
- 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, Article 100512. https://doi.org/10.1016/j.ascom.2021.100512 [details]
- Ruhe, D., Wong, K., Cranmer, M., & Forré, P. (2022). Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. In Machine Learning and the Physical Sciences: Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) : December 3, 2022 ML4PS. https://doi.org/10.48550/arXiv.2211.09008 [details]
2021
- 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]
- Boelrijk, J., Pirok, B., Ensing, B., & Forré, P. (2021). Bayesian optimization of comprehensive two-dimensional liquid chromatography separations. Journal of Chromatography A, 1659, Article 462628. Advance online publication. https://doi.org/10.1016/j.chroma.2021.462628 [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]
2020
- Apostol, A. C., Stol, M. C., & Forré, P. (2020). FlipOut: Uncovering Redundant Weights via Sign Flipping. In L. Cao, W. Kosters, & J. Lijffijt (Eds.), BNAIC/BeNeLearn 2020: proceedings : Leiden, the Netherlands, November 19-20, 2020 (pp. 15-29). Universiteit Leiden. http://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/papers/BNAICBENELEARN_2020_Final_paper_25.pdf [details]
2019
- 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 Article 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 Article 253 AUAI Press. https://arxiv.org/abs/1810.01118 [details]
2018
- 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]
2023
- Federici, M., Ruhe, D. J. J., & Forré, P. D. (2023). On the Effectiveness of Hybrid Mutual Information Estimation. ArXiv. https://doi.org/10.48550/arXiv.2306.00608
2022
- Boelrijk, J., Ensing, B., & Forré, P. (2022). Multi-Objective Optimization via Equivariant Deep Hypervolume Approximation. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2210.02177v1 [details]
- Federici, M., Forre, P., & Tomioka, R. (2022). An Information-theoretic Approach to Distribution Shifts. 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. 21, pp. 17628-17641). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2106.03783 [details]
- Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786 [details]
- Lang, L., Baudot, P., Quax, R., & Forré, P. (2022). Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2202.09393v1 [details]
- Maile, K., Wilson, D. G., & Forré, P. (2022). Architectural Optimization over Subgroups for Equivariant Neural Networks. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2210.05484 [details]
- Pandeva, T., & Forré, P. (2022). Multi-View Independent Component Analysis with Shared and Individual Sources. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2210.02083v1 [details]
- Pandeva, T., Bakker, T., Naesseth, C. A., & Forré, P. (2022). E-Valuating Classifier Two-Sample Tests. ArXiv. https://doi.org/10.48550/arXiv.2210.13027 [details]
2021
- Ilse, M., Tomczak, J. M., & Forré, P. (2021). Selecting Data Augmentation for Simulating Interventions. Proceedings of Machine Learning Research, 139, 4555-4562. https://proceedings.mlr.press/v139/ilse21a.html [details]
- 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]
- Ruhe, D., & Forré, P. (2021). Self-Supervised Inference in State-Space Models. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2107.13349 [details]
- Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2021). Coordinate Independent Convolutional Networks: Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2106.06020 [details]
2024
- Pandeva, T. P., Forré, P. D., Ramdas, A., & Shekhar, S. (2024). Deep anytime-valid hypothesis testing.
2023
- Miller, B. K., Federici, M., Weniger, C., & Forré, P. D. (2023). Simulation-based Inference with the Generalized Kullback-Leibler Divergence. Paper presented at ICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling, Honolulu, Hawaii, United States. https://arxiv.org/abs/2310.01808
2022
- Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539 [details]
- Maile, K., Wilson, D. G., & Forré, P. (2022). Towards architectural optimization of equivariant neural networks over subgroups. Paper presented at NeurIPS 2022 Workshop: NeurReps, New Orleans, Louisiana, United States. https://openreview.net/forum?id=KJFpArxWe-g [details]
- 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
- 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. https://openreview.net/pdf?id=B1xwcyHFDr
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. https://arxiv.org/abs/1807.04689 [details]
Prijs / subsidie
- Groot, A., Ellis-Monaghan, J., Forré, P. & Samanipour, S. (2024). MMD hub grant: Predicting and designing olfactory molecules. https://hims.uva.nl/shared/faculteiten/en/faculteit-der-natuurwetenschappen-wiskunde-en-informatica/news/2024/07/nine-research-projects-chos…
- Samanipour, S., Praetorius, A. & Forré, P. (2023). AI4Plastics.
2024
- Boelrijk, J. H. M. (2024). Bayesian optimization for liquid chromatography separations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2021
- Forré, P. (2021). Quasi-Measurable Spaces. ArXiv. https://arxiv.org/abs/2109.11631 [details]
- Forré, P. (2021). Transitional Conditional Independence. ( v1 ed.) ArXiv. https://arxiv.org/abs/2104.11547v1 [details]
2020
- Bongers, S., Forré, P., Peters, J., Schölkopf, B., & Mooij, J. M. (2020). Foundations of Structural Causal Models with Cycles and Latent Variables. (v4 ed.) ArXiv. https://arxiv.org/abs/1611.06221v4 [details]
- Helwegen, R., Louizos, C., & Forré, P. (2020). Improving Fair Predictions Using Variational Inference In Causal Models. (v1 ed.) ArXiv. https://arxiv.org/abs/2008.10880 [details]
- Ilse, M., Tomczak, J. M., & Forré, P. (2020). Selecting Data Augmentation for Simulating Interventions. (v4 ed.) ArXiv. https://arxiv.org/abs/2005.01856 [details]
- Verdenius, S., Stol, M., & Forré, P. (2020). Pruning via Iterative Ranking of Sensitivity Statistics. (v2 ed.) ArXiv. https://arxiv.org/abs/2006.00896 [details]
2017
- Forré, P., & Mooij, J. M. (2017). Markov Properties for Graphical Models with Cycles and Latent Variables. Informatics Institute, University of Amsterdam. https://arxiv.org/abs/1710.08775 [details]
2022
- Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks. Zenodo. https://doi.org/10.5281/zenodo.6364941
- Lippert, F., Kranstauber, B., Forré, P. & van Loon, E. . (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks. Zenodo. https://doi.org/10.5281/zenodo.6874789
2021
- Miller, B., Cole, A., Forré, P., Louppe, G. & Weniger, C. (2021). Truncated Marginal Neural Ratio Estimation - Data. Zenodo. https://doi.org/10.5281/zenodo.5592427
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list. -
Nevenwerkzaamheden
- Geen nevenwerkzaamheden
Boelrijk, J., Molenaar, S. R. A., Bos, T. S., Dahlseid, T. A., Ensing, B., Stoll, D. R., Forré, P., & Pirok, B. W. J. (2024). Enhancing LC×LC separations through multi-task Bayesian optimization. Journal of Chromatography A, 1726, Article 464941.