Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 65
Number of items: 65
  • Open Access
    Pandeva, T., Jonker, M. J., Hamoen, L., Mooij, J. M., & Forré, P. (2025). Robust Multi-view Co-expression Network Inference. Proceedings of Machine Learning Research, 275, 490-513. https://proceedings.mlr.press/v275/pandeva25a.html
  • Open Access
    Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Liu, C., Ruhe, D., Eijkelboom, F., & Forré, P. (2024). Clifford Group Equivariant Simplicial Message Passing Neural Networks. In The Twelfth International Conference on Learning Representations: ICLR 2024 https://doi.org/10.48550/arXiv.2402.10011
  • Open Access
    Rateike, M., Valera, I., & Forré, P. (2024). Designing Long-term Group Fair Policies in Dynamical Systems. In ACM FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency : June 3rd-6th 2024, Rio de Janeiro, Brazil (pp. 20–50). The Association for Computing Machinery. https://doi.org/10.1145/3630106.3658538
  • Open Access
    Federici, M., Forré, P., Tomioka, R., & Veeling, B. S. (2024). Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. In The Twelfth International Conference on Learning Representations: ICLR 2024 https://doi.org/10.48550/arXiv.2309.07200
  • Open Access
    Zhdanov, M., Ruhe, D., Weiler, M., Lucic, A., Forré, P. D., Brandstetter, J., & Forré, P. (2024). Clifford-steerable convolutional neural networks. Proceedings of Machine Learning Research, 235, 61203-612228. https://proceedings.mlr.press/v235/zhdanov24a.html
  • Open Access
    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
  • Open Access
    Boelrijk, J. H. M. (2024). Bayesian optimization for liquid chromatography separations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Jazbec, M., Forré, P., Mandt, S., Zhang, D., & Nalisnick, E. (2024). Early-Exit Neural Networks with Nested Prediction Sets. Proceedings of Machine Learning Research, 244, 1780-1796. https://proceedings.mlr.press/v244/jazbec24a.html
  • Open Access
    Federici, M., Ruhe, D., & Forré, P. (2023). On the Effectiveness of Hybrid Mutual Information Estimation. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2306.00608
Page 2 of 7