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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]
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]
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]
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]
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., Forré, P., Welling, M., & Mooij, J. M. (2022). Combined Observational and Interventional Data through Causal Reductions. ArXiv. https://arxiv.org/abs/2103.04786[details]
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]
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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