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Results: 65
Number of items: 65
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Helwegen, R., Louizos, C., & Forré, P. (2020). Improving Fair Predictions Using Variational Inference In Causal Models. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2008.10880
  • Open Access
    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
  • Open Access
    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://doi.org/10.48550/arXiv.1611.06221
  • Open Access
    Ilse, M., Tomczak, J. M., & Forré, P. (2020). Selecting Data Augmentation for Simulating Interventions. (v4 ed.) ArXiv. https://doi.org/10.48550/arXiv.2005.01856
  • Open Access
    Ullrich, K. (2020). A coding perspective on deep latent variable models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Verdenius, S., Stol, M., & Forré, P. (2020). Pruning via Iterative Ranking of Sensitivity Statistics. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2006.00896
  • Open Access
    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
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