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Results: 58
Number of items: 58
  • Open Access
    Lang, L. (2026). Mathematical developments in abstract information theory and safe reward learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van Henten, G. B., Boelrijk, J., Kattenberg, C., Bos, T. S., Ensing, B., Forré, P., & Pirok, B. W. J. (2025). Comparison of optimization algorithms for automated method development of gradient profiles. Journal of Chromatography A, 1742, Article 465626. https://doi.org/10.1016/j.chroma.2024.465626
  • Open Access
    Federici, M. (2025). Information theory for representation learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Lippert, F. (2025). From weather radars to bird migration fluxes: Process-guided machine learning for spatio-temporal forecasting and inference. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Ruhe, D. J. J. (2025). Structured deep learning with applications in astrophysics. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Pandeva, T. P. (2025). Machine learning for multi-source data integration. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    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
    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
    Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam].
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