Dr. P.D. (Patrick) Forré
Faculty of Science
Informatics Institute
Visiting address
- Science Park 904
- Room number: C3.226
Postal address
- Postbus 94323
1090 GH Amsterdam
Contact details
-
Publications
- refereed (12)
- academic (2)
- conference contributions (5)
- supervised theses (1)
- working papers / preprints (10)
2022
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]
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]
- Apostol, A., Stol, M. C., & Forré, P. D. (Accepted/In press). Pruning by leveraging training dynamics. AI Communications. https://doi.org/10.2133/AIC-210127
- Boelrijk, J., Pirok, B., Ensing, B., & Forré, P. (2021). Bayesian optimization of comprehensive two-dimensional liquid chromatography separations. Journal of Chromatography A, 1659, [462628]. 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]
- Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P. D., & Welling, M. (2021). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. In Advances in Neural Information Processing Systems 34 Proceedings: NeurIPS 2021 NIPS Foundation. https://papers.nips.cc/paper/2021/hash/67d96d458abdef21792e6d8e590244e7-Abstract.html
- Miller, B. K., Cole, A. E., Forré, P. D., Louppe, G., & Weniger, C. (2021). Truncated Marginal Neural Ratio Estimation. In Advances in Neural Information Processing Systems 34 Proceedings: NeurIPS 2021 NIPS Foundation. https://papers.nips.cc/paper/2021/hash/01632f7b7a127233fa1188bd6c2e42e1-Abstract.html
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 [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]
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]
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]
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
2022
- Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combined Observational and Interventional Data through Causal Reductions. ArXiv. https://arxiv.org/abs/2103.04786 [details]
2021
- Forré, P. D. (2021). Quasi-Measurable Spaces. https://arxiv.org/abs/2109.11631
- Forré, P. D. (2021). Transitional Conditional Independence. https://arxiv.org/abs/2104.11547
- Ruhe, D. J. J., & Forré, P. D. (2021). Self-Supervised Inference in State-Space Models. https://arxiv.org/abs/2107.13349
- Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2021). Coordinate Independent Convolutional Networks: Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. arXiv.org. https://arxiv.org/abs/2106.06020 [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.org. 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.org. https://arxiv.org/abs/2008.10880 [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]
- Verdenius, S., Stol, M., & Forré, P. (2020). Pruning via Iterative Ranking of Sensitivity Statistics. (v2 ed.) arXiv.org. https://arxiv.org/abs/2006.00896 [details]
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. -
Ancillary activities
- No ancillary activities
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].