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Results: 63
Number of items: 63
  • Open Access
    Federici, M. (2025). Information theory for representation learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    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
    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
    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
    Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Boelrijk, J. H. M. (2024). Bayesian optimization for liquid chromatography separations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Ruhe, D., Brandstetter, J., & Forré, P. (2023). Clifford Group Equivariant Neural Networks. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 62922-62990). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-2748
  • Open Access
    Boelrijk, J., Ensing, B., Forré, P., & Pirok, B. W. J. (2023). Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization. Analytica Chimica Acta, 1242, Article 340789. https://doi.org/10.1016/j.aca.2023.340789
  • Open Access
    Forré, P., Miller, B. K., & Weniger, C. (2023). Contrastive Neural Ratio Estimation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 5, pp. 3262-3278). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2210.06170
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