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Results: 62
Number of items: 62
  • Open Access
    Pandeva, T., Bakker, T., Naesseth, C. A., & Forré, P. (2022). E-Valuating Classifier Two-Sample Tests. ArXiv. https://doi.org/10.48550/arXiv.2210.13027
  • Open Access
    Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. https://doi.org/10.1111/2041-210X.14007
  • Open Access
    Maile, K., Wilson, D. G., & Forré, P. (2022). Architectural Optimization over Subgroups for Equivariant Neural Networks. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2210.05484
  • Open Access
    Ruhe, D., Wong, K., Cranmer, M., & Forré, P. (2022). Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. In Machine Learning and the Physical Sciences: Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) : December 3, 2022 ML4PS. https://doi.org/10.48550/arXiv.2211.09008
  • Open Access
    Cole, A., Forre, P., Louppe, G., Miller, B. K., & Weniger, C. (2022). Truncated Marginal Neural Ratio Estimation. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 1, pp. 129-143). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/01632f7b7a127233fa1188bd6c2e42e1-Abstract.html
  • Open Access
    Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539
  • Open Access
    Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786
  • Open Access
    Bos, T. S., Boelrijk, J., Molenaar, S. R. A., Veer, B. V. ., Niezen, L. E., van Herwerden, D., Samanipour, S., Stoll, D. R., Forré, P., Ensing, B., Somsen, G. W., & Pirok, B. W. J. (2022). Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. Analytical Chemistry, 94(46), 16060-16068. https://doi.org/10.1021/acs.analchem.2c03160
  • Open Access
    Federici, M., Forre, P., & Tomioka, R. (2022). An Information-theoretic Approach to Distribution Shifts. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 21, pp. 17628-17641). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2106.03783
  • Open Access
    Boelrijk, J., Ensing, B., & Forré, P. (2022). Multi-Objective Optimization via Equivariant Deep Hypervolume Approximation. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2210.02177v1
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